text:
\section{Introduction}
"Beauty is in the eye of the beholder", is a common phrase used in English. This phrase becomes more true when we consider something like art \cite{kumar2010beauty}. Art is something very human by nature. It is highly subjective, entirely based on the observer and their experiences \cite{johnston1993beauty}. For example, it is hard for some people to accept rap as a form of music, and as a form of art \cite{shushterman1991rap}. Art always has a pattern that is recognizable, while still containing some irregularities \cite{field2009art}. This mixture of patters and irregularities are able to please us and we start to like that form of art.

Human beings, for the longest time, have believed that art is the forefront where a machine may never beat them \cite{varshney2013cognition}. But, with the advent of powerful computers and state of the art machine learning algorithms, there might be more prominent AI artists in the future \cite{besold2015creativity}. Since machines don't need rest, and an AI could generate 1000s of art pieces per hour, there will be increased efficiency and cost-effectiveness in art based sectors \cite{toivonen2020creativity}. Generative Adversial Networks (GAN) \cite{goodfellow2014generative}, are very popular these days, especially for creating hyper-realistic faces \cite{gauthier2014conditional}. These systems have huge face datasets to learn from \cite{gao2007face}\cite{kasinki2008face}\cite{ma2015face}. An example of such a system is DeepFake \cite{korshunova2017face}, which superimposes the generated face over a subject's face in an image or video so that it seems like some other person is present in the image or video. Another example is Neural Style Transfer \cite{li2017style}, which learns the art style from one image and applies the style to another image effectively transferring the style. This technique has been used to recreate the art style of famous painters \cite{gatys2016style}.

A more technical term for machines creating art, is Computational Creativity \cite{colton2012creativity}. It is an interdisciplinary field spanning topics like AI, cognition, arts, design and philosophy \cite{minsky1967creativity}. It is the synthesis of art, in a partially or fully automated way. This field of study focuses on creating algorithms that mimic human creativity, understanding human creativity, mathematically defining creativity, or create systems that assist and enhance the human creative process \cite{gero2000creativity}. Music is probably one of the toughest computational creativity problems, as it is difficult to analyze by a computer \cite{leach1995nature}. It is easier to analyze an image, as there is no temporal component, and the whole image can be observed at once. It's local and global dependencies can be easily analyzed. Whereas, in the case of music, the temporal dimension, and the fact that it is highly coherent and rhythmic, poses even more problems for the computer. The task of generating melodic audio which sound realistic is a tough job and is an open problem in the field of machine learning \cite{papadopoulos1999composition}. Imagine a DJ who does not only mix music but also composes them on the fly. Another example is generating music based on the current mood of the user or even replicate the style of famous musicians and generate new music. The possibilities are endless.

This paper explores a methodology for generating unique music, that is melodic, using deep learning. The methodology involves a bi-directional LSTM with attention mechanism. This system learns the melodic and rhythmic patterns and dependencies in order to generate novel music as output. MIDI \cite{moog1986midi}, a popular high level representation for music has been used.

\section{Algorithmic Composition}
Algorithmic composition is the process of using some rules and combining parts of music to form a whole \cite{nierhaus2009composition}. It is not necessarily computer based, an example being Mozart's musical dice game \cite{hedges1978dice}.

Algorithmic composition has been around for centuries, with Pythagoras suggesting Music and Mathematics being the same \cite{fauvel2006pythagoras}. One of the first computational models used for generating music was using Markov chains \cite{mcalpine1999music}. Other older techniques include species counterpoint, d'Arezzo's Micrologus \cite{sullivan1989micrologus}, Schoenberg's twelve-tone technique \cite{dahlaus1987music}, or Cage's aleatoric music \cite{jeongwon2002aleatoric}. Algorithmic composition is not restricted to just computational means, as seen by the above mentioned methods, but that is not the focus point of this paper. The focus will be on current AI techniques, and more specifically, NLP techniques.

Some other algorithms, including fractals \cite{hsu1991music}, statistical models \cite{conklin2003music}, L-systems \cite{worth2005music}, and even random real world data, can in fact be used as a basis for music generations. This is because all of them have some sort of mathematical patterns. Generally algorithmic composition is either music composed by a machine, or music created with the help of a machine.

Using the most prominent features, algorithmic composition may be categorised as
\begin{enumerate}
    \item \textbf{Mathematical Models}: These refer to stochastic processes, and more specifically Markov chains, used for real-time algorithmic composition, due to it's low complexity. An example of such a model used for algorithmic composition is Cybernetic Composer \cite{ames1992cyber}.
    \item \textbf{Knowledge based Systems}: These are systems which use symbols and specified rules with constraint to achieve algorithmic composition. An example of this is CHORAL \cite{ebcioglu1988choral}. Building systems like these for music is difficult and time taking.
    \item \textbf{Grammars}: A grammar set for a musical language can be defined, and it's rules used randomly to generate unique music. An Augmented Transition Network (ATN) \cite{woods1970atn} is used to accomplish this task. The music generated may not be of optimal quality, as the semantics defined may not be robust.
    \item \textbf{Evolutionary Methods}: Genetic Algorithms (GA) \cite{whitley1994genetic} are used in this approach. They either use an objective fitness function or a human fitness function. Since music is subjective, the latter may produce undesirable results \cite{horner1991genetic}.
    \item \textbf{Systems which Learn}: These consists of machine learning based systems, especially ANNs \cite{browne2001music}. This paper explores usage of an LSTM based learning system \cite{mangal2019lstm}.
    \item \textbf{Translational Models}: Information from non-musical media is translated to musical media. For example, using time series data generally used for load forecasting and instead restructuring the dimensions as pitch and scale.
    \item \textbf{Hybrid Systems}: Using any of these systems in combination forms a hybrid system. Basically an ensemble model for algorithmic composition.
\end{enumerate}

\section{Related Work}
Generating music is a task that has researchers all around the world producing new ideas. Even in some cases, the main focus is not on music generation but when conditioned on musical dataset, music can be generated. Such is the case with WaveNet \cite{oord2016wavenet}.

"WaveNet" is a probabilistic autoregressive generative model which can generate raw audio. It was developed by Deepmind and has been used in Text-To-Speech applications. The output produced by WaveNet is very realistic and the voices it creates are almost humanlike. Its effectiveness can be summarized by the high Mean Opinion Score (MOS) mentioned in the paper in which it was introduced and the fact that it has been implemented as the voice of Google Assistant. It is not limited to just Text-To-Speech applications as when the developers trained it with musical dataset, realistic sounding rhythmic audio was generated which can easily be identified as music. The parallel version of WaveNet is very fast \cite{oord2018pwavenet}. It can generate 20 seconds of audio in just 1 second. Since it is a Convolutional Neural Network, training it is very easy for the computer as opposed to Recurrent Neural Network which is generally suited to handle sequences of data such as audio.

Another paper, "A Neural Parametric Singing Synthesizer" or NPSS \cite{blaauw2017npss}, uses a modified version of WaveNet to generate music based on features produced by a parametric vocoder. This produces great results which is expected from WaveNet.

Recurrent Neural Networks have also been used for the purpose of music generation. One such application can be seen in the paper "LSTM Based Music Generation System" \cite{mangal2019lstm}. Since audio samples at any instant highly depend on previous samples for coherent audio, Recurrent Neural Network, especially Long Short-term Memory can be used to take care of these dependencies. This approach works well with classical and jazz music because these are more free-form and without definite song structure like verse, chorus and bridge. This is because it only needs to know the recent context of notes to generate the next note.

Generative Adversarial Networks (GANs) can also be used for the purpose of music generation. One such system is "WGANSing" \cite{chandna2019wgan}, a Multi-Voice Singing Voice Synthesizer Based on the Wasserstein-GAN – It is a General Adversarial Network. It employs two networks. One called the generator which generates new music and tries to fool the other network and the other called the discriminator which tries to discriminate between real musical recording and a generated music. After each epoch of training, both the networks get better at their jobs and the generator can be used to generate music.

OpenAI has also worked in this field and created MuseNet. It uses the same general-purpose unsupervised technology as GPT-2 \cite{radford2019gpt}, a large-scale transformer model trained to predict the next token in a sequence, whether audio or text. It is able to extend a music whose starting fragment is given as input or it can generate music from scratch. The live example on their blog shows its performance. There we can select the target style in which the music is to be generated and the starting fragment of a song (which can be from scratch as well).

Some applications such as jukedeck.com (currently offline), aiva.ai, ampermusic.com and many more have commercialized their music generation system and have been deployed as a service to generate custom royalty free music for its users.

Frameworks such as "Magenta" have been made available to the world for researchers to work in the field of generating art by a computer. Magenta in particular, is an open source research project exploring the role of machine learning as a tool in the creative process. It is available for Python and JavaScript, built with TensorFlow. It includes utilities for manipulating source data (primarily music and images), using this data to train machine learning models, and finally generating new content from these models.

Another paper, "The challenge of realistic music generation: modelling raw audio at scale" \cite{dieleman2018music} explores Autoregressive Discrete Autoencoders (ADAs) as a means to enable autoregressive models such as WaveNet to capture long-range correlations in waveforms which otherwise captures local signal structures. The use of autoencoders in this aspect has also been demonstrated by the YouTube channel Code Parade which developed the Neural Composer. It shows the use of Principal Component Analysis to choose the top 40 principal components and use them as control sliders to generate custom music.

\section{The Problem of Music Generation}
Music often brings nostalgia in humans. It is an art form with which we attach emotions. This paper aims to generate realistic music in a sense that it can be recognized as music made by humans. In order to achieve this goal, the problem statement may be divided into several subtasks.

\begin{enumerate}

    \item The system should be able to generate new audio using high-level representation such as MIDI (The first step could be to generate MIDI).

    \item The system should learn the rhythmic sequences of music during the training phase and produce similar but novel music as output. For audio, each sample depends on the context of the previous samples. This is even more true in case of music. Generally, the context of the music samples depends on the context of all of the previous samples. This is best implemented in WaveNet and made computationally efficient by using Dilated Convolutions which makes large skips in the data to get a larger receptive view of the data.

    \item Based on some input parameters such as the variance control of the principal components in the Neural Composer, the system should be able to produce appropriate results. These components change the output, ever so slightly and when they are changed in combination, newer music is generated.

    \item Overall, the output should be melodic. This is obvious as it is the goal of the paper.

    \item The output should have a decent MOS (Mean Opinion Score, i.e., if people are able to identify that the music is generated by a computer). Since, evaluating audio is difficult as it is highly subjective. The best way to check the performance of a music generation system is to conduct a survey in which, human participants try to guess if a music being played has been generated by a computer or it is an actual human performance.

\end{enumerate}

Some of the optional requirements are:

\begin{enumerate}

    \item Inputs such as mood, genre, instrument, mode, key, tempo, etc. could be input parameters. This requires labelled data and can be difficult.

    \item Future objective could be to integrate lyrics generator and vocal synthesizer.

\end{enumerate}

\section{Dataset}

Some of the dataset that have been identified are:

\begin{enumerate}

    \item \textbf{The MAESTRO Dataset}: MAESTRO \cite{hawthorne2018maestro} (MIDI and Audio Edited for Synchronous TRacks and Organization) is a dataset composed of over 200 hours of paired audio and MIDI recordings from ten years of International Piano-e-Competition. Audio and MIDI files are aligned with ~3 millisecond accuracy and sliced to individual musical pieces, which are annotated with composer, title, and year of performance. Uncompressed audio is of CD quality or higher (44.1–48 kHz 16-bit PCM stereo). The labels are not according to our requirement. The size of the dataset in two formats are: WAV: 103 GB (122 GB uncompressed), MIDI: 57 MB (85MB uncompressed).

    \item \textbf{The Million Song Dataset}: The core of the dataset \cite{bertin2011million} is the feature analysis and metadata for one million songs, provided by The Echo Nest. The dataset does not include any audio, only the derived features. Note, however, that sample audio can be fetched from services like 7digital, using code. Songs are tagged (artists, release, album, etc.). It contains more usable labels, such as danceability, key, mode, tempo, etc. for our purpose. The size of the dataset is 280 GB (Full Dataset, i.e., One Million Songs) or 1.8 GB (1\% or 10000 songs).

    \item \textbf{MagnaTagATune Dataset}: This dataset \cite{law2009input} contains the most versatile collection of labels. But the labels are often unorganized and duplicated like a song has been labeled for both classic and classical genre. Extensive preprocessing will be required to work with this dataset. It can be used for genre, instrument and mood classification for music.

    \item \textbf{Creating Custom Dataset}: Adequate dataset for the optional objectives do not exist so a custom dataset has to be created. This requires time and effort for manually labeling the data.

\end{enumerate}

The format of these datasets is either WAV which is directly in the audio domain or MIDI which gives the idea of playing what note at what time. Both of these formats are good but using MIDI is generally easier.

\section{Data Representation}
In order to generate music, it is necessary to understand what the data is. Music is generally represented in the following ways.

\subsection{Notes}
A note \cite{strunk1942music} is the symbolic representation of musical sounds. This can be seen in Fig.\ref{fig:1} Notes may be represented in English as A, B, C, D, E, F and G. This totals up to 7 different notes. Another popular representation is Do, Re, Mi, Fa, So, La and Ti. The octave (eighth note), is same as the first, with a double the frequency value, meaning it has a higher pitch. In addition to the note symbol, written notes may also have a note value. This note value determines the notes duration.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/notes.jpg}
\caption{Data Representation: Musical Notes}
\label{fig:1}
\end{figure}

The frequency of these notes are measured in hertz (Hz), as mechanical systems produce them. There are 12 notes with fixed frequencies, defined around the central note A$_4$. This note has frequency 440 Hz.

\subsection{Raw Audio}
Uncompressed audio is stored in raw form is called Raw Audio \cite{tzanetakis2000audio}. Fig.\ref{fig:2} shows the waveform. This file has no header, and hence cannot be played without some user input. So, generally file formats like WAV and AIFF are used, as they are lossless and have nearly the same size. Other lossless formats include FLAC, ALAC and WMA Lossless. Most popular of these are the WAV and FLAC formats.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/raw.png}
\caption{Data Representation: Raw Audio}
\label{fig:2}
\end{figure}

WAV (Waveform Audio File Format) is an audio file format based on the RIFF bit-stream format. It is similar AIFF as both are based o RIFF. It is typically uncompressed and uses linear pulse-code modulation for bit-stream encoding. Although it is possible to store compressed audio using the WAV format. WAV format is generally used when the best audio quality is required. Generally audio is encoded at 44.1 kHz and 16 bit sample rate.

FLAC (Free Lossless Audio Codec) is a lossless format for storing audio. This codec allows for lossless compression ranging between 50\% to 70\%. It can be easily decoded and streamed,

\subsection{MIDI}
MIDI \cite{moog1986midi} (Musical Instrument Digital Interface) is a universal protocol/interface for many electronic instruments and computers. It allows for ease in recording various instruments and simultaneously interfacing with them via a computer. The file format for this interface is called SMF (Standard MIDI File), and usually has a .mid extension. These files are very small and can be easily transferred between various devices. The header of an SMF contains a lot of essential information like tempo or pitch. Actual audio is not stored in SMF, instead only storing information like pitch and tempo. This is the reason for their extremely small sizes and high portability. This even allowed for licensed MIDI files on FLoppy drives. The main advantage of MIDI is that the instrument type can be easily changed with various software, and that MIDI is great for building the basis of a good soundtrack. Fig.\ref{fig:3} shows the graphical representation of MIDI.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/midi.png}
\caption{Data Representation: MIDI}
\label{fig:3}
\end{figure}

\subsection{Piano Rolls}
Piano Rolls \cite{shi2017piano} are rolls of papers with holes in them as can be seen in Fig.\ref{fig:4} The holes are the data for the notes. They are the logical predecessor to MIDI and an inspiration for it. In softwares, piano rolls generally refer to the visual representation of MIDI data. It is theoretically possible to use this visual data as input for some image based generation model, like GANs. This may be explored in future work.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/piano.jpg}
\caption{Data Representation: Piano Roll}
\label{fig:4}
\end{figure}

\section{Data Encoding}
In this paper a custom data set was used, comprising of 92 MIDI files. These are modern video game soundtracks.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/encode.png}
\caption{Encoding Technique}
\label{fig:5}
\end{figure}

As MIDI files cannot be directly used in an LSTM, they need to be encoded into an appropriate input vector. For this purpose, one hot encoding methodology is used. For each note in the MIDI file, a corresponding true/false value is set. For example, if the note 'A' is present at a specific time step, then the column value corresponding to 'A' will be set to true, and all other set to false. This can be observed in the Fig.\ref{fig:5}, where each column represents an encoded vector for each note present in the song. These generated vectors become the input to the proposed model.

\section{Proposed Model}
A seq2seq \cite{sutskever2014seq} model with attention mechanism \cite{vaswani2017attention} is used for music generation. This is due to seq2seq being able to generate one sequence from another without running into the vanishing gradient problem. It uses an encoder layer and a decoder layer. The encoder layer converts an item into it's corresponding hidden vector and context. The decoder does the opposite of this. In between these two layers is the attention layer. The attention layer helps remember short as well as long term dependencies.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/model.png}
\caption{Proposed Model}
\label{fig:6}
\end{figure}

The seq2seq model used is an LSTM. The Luong attention mechanism is used with it. The encoder layer consists of a bi-directional LSTM layer and the decoder layer uses a regular LSTM layer. The encoded input vector is converted to hidden states by encoder. This is fed into the attention layer, which produces a context vector. Finally, the context vector thus produced will be used to decode the output sequence The model has been shown in Fig.\ref{fig:6}. It's specifications can be seen in Fig.\ref{fig:7}.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/keras.png}
\caption{Keras Model Specifications}
\label{fig:7}
\end{figure}

\section{Experimental Results}
The Google Colaboratory platform was used for training the model. This platform provides an Intel(R) Xeon(R) CPU @ 2.20GHz, 12 GB of RAM, and an NVIDIA Tesla K80 GPU.

The training took around 4 hours on this system to achieve an accuracy of 93.02\% at 31 epochs. At this point the training was stopped to avoid overfitting, so as to ensure variety in the generated music. This can be seen in figures \ref{fig:10} and \ref{fig:11}.

The model was trained on a dataset comprising of only 92 MIDI files, which essentially is a small dataset. This was done for faster prototyping. The workflow can be seen in Fig.\ref{fig:8}.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/twork.png}
\caption{Training Workflow}
\label{fig:8}
\end{figure}

After training, the model can be used for prediction, i.e., music generation. The input and output of the system are as follows.

\begin{enumerate}

    \item \textbf{Input}: A random note or a sequence of notes.

    \item \textbf{Output}: The random note or the sequence of notes extended to a length of nearly two minutes. This sequence is rhythmic.

\end{enumerate}

The prediction workflow is shown in Fig.\ref{fig:9}. The output generation takes approximately 4-5 seconds to complete. This means it is highly efficient.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/pwork.png}
\caption{Prediction Workflow}
\label{fig:9}
\end{figure}

The model produces a sequence of One-Hot encoded vectors which need to be converted back into MIDI format. To do this, reverse process of the initial One-Hot encoding can be done. Since the produced results are audio files, no image is produced and cannot be shown on paper. The model generates results which sound fairly realistic and the music thus produced was not actually given as input. Though, some nuances of the input songs can be observed. This means that we can tell that the model has learnt from the input songs.

The results can be improved by training on a much larger dataset. In our attempt, we trained on only 92 songs for faster prototyping but this can be extended to huge datasets as well.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/acc.png}
\caption{Model Accuracy}
\label{fig:10}
\end{figure}

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/loss.png}
\caption{Model Loss}
\label{fig:11}
\end{figure}

A valid question may arise. What if the same starting note is used multiple times? The answer is that it is most likely to produce the same output but this can be solved easily. The input can be extended to a sequence of notes. This sequence of notes can make space for many random combinations of starting notes.

Further, for better availability, the entire system has been converted into a web app. It can be reached at \url{https://research.jamlab.in/music-generator} . The web app has a highly intuitive interface as can be seen in Fig.\ref{fig:12}. There is a button which says "Generate Song". Upon pressing the button, the model will be invoked and a midi file will be generated. The generated midi file can be listened to using the midi player on the page itself. Once a file is played, it is visualized with a virtual piano displaying what keys are pressed to play the song.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/web.png}
\caption{Web Application}
\label{fig:12}
\end{figure}

The performance of this model was evaluated using a mean opinion score (MOS) test. 100 subjects were chosen for the survey, and 5 random songs, generated by the model, were played for each of them. The subjects were asked to rate the songs on a five-point scale (1:Very Poor, 2:Poor, 3:Average, 4:Good, 5:Very Good). A total of 500 different songs were evaluated this way, and a mean of the scores was evaluated. This MOS value came out to be 3.4. The survey has been summarized in Fig.\ref{fig:13}.

\begin{figure}
\centering
\captionsetup{justification=centering}
\includegraphics[width=\textwidth,height=\textheight,keepaspectratio]{Images/MOS.png}
\caption{MOS or Mean Opinion Score (Y-Axis shows the number of songs generated)}
\label{fig:13}
\end{figure}

Fig.\ref{fig:13} suggests that most people think that the generated music is good. It can also be seen that many people think that it was average, but the generated compositions may instead be used as inspiration for new music.

\section{Conclusion}
A deep learning model capable of generating specific type of music by learning from MIDI data was created. The music is unique, and also pleasant, and overall the scores produced by the model have a good opinion value. Hence, it is possible to generate aesthetically pleasing music using a high level representation such as MIDI.

However, it can only generate a specific style/genre/mood of music that it has been trained on. These parameters, and using a larger and more comprehensive data set will be explored in future work.

\section{Future Work}
A lot of improvements can be made to the proposed model. This includes ability to generate specific mood/style/genre of music, training on a much larger dataset, and also exploring possibility of having different instruments and multiple tracks.

The piano roll based approach is also something that can be explored. Possibility of having lyric generation and singing synthesis could lead to a fully automated music production system.

\input{references}

\end{document}

redpajama_set_name: RedPajamaArXiv


(AllHipHop News) A few weeks ago, some observers were ready to declare Cardi B's career over. However, a violent incident at New York Fashion Week hasn't stopped the Bronx rapstress from collecting another chart record.
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The Invasion of Privacy album creator celebrated her latest accomplishment on Instagram. She thanked Maroon 5 for adding her to the song.


text:
9 Most Shocking Revelations From the 'Bachelor Nation' Tell-All Book
Jessica Napoli March 14, 2018, 5:00 pm
ABC/Paul Hebert
THE BACHELOR - CHRIS HARRISON, ARIE LUYENDYK JR.
Bachelor fans beware. After reading Amy Kaufman's tell-all (yet unauthorized) book, Bachelor Nation: Inside the World of America's Favorite Guilty Pleasure, you may not have the same feelings about the show.
Aaaand it's real. #BachelorNation #25days
A post shared by Amy Kaufman (@amykinla) on Feb 9, 2018 at 4:16pm PST
In her stunning debut, Kaufman—a journalist with the Los Angeles Times—pulls the curtain back on the ABC franchise. Thanks to a number of interviews with producers and conversations with past contestant, she has plenty of insider details to share, many of them shocking and controversial.
'The Bachelor': What's Next for Arie, Lauren and Becca?
After an emotionally rough finale, they're picking up the pieces and moving on.
Here are the 9 biggest revelations from Bachelor Nation, in order from least to most shocking:
The Bachelor and Bachelorette contestants get paid to be there. They can make a salary that's close to the jobs they leave behind, if they negotiate in the right way.
The money doesn't stop just because you leave the show, either. Booted contestants could make anywhere between $250 and $10,000 per Instagram post, so don't make fun of that Fit Tea life ever again.
Producers own the rights to the Neil Lane ring and the TV wedding for two years after the show. So that means if a couple breaks up, they cannot sell the ring and if the couple gets married, they get $10,000/hour to televise it.
The memorable dates that take place all around the world don't cost ABC anything. Yes, it all revolves around free publicity. Kaufman claims the show encourages producers to negotiate with vendors (hotels, helicopters, horseback riding) for free stuff in exchange for airtime.
Michael Carroll, an ex-producer, told Kaufman that producers label contestants immediately. "You'd pre-categorize (contestants) and have some shorthand as to who they were," he said. "Mom. Southern Belle. The cheerleader. The b*tch. We all called them ridiculous names. The fat one, the hot one, the crier."
Kaufman explains how editors use a method called "Frankenbiting" to create the perfect picture. She describes it as a "sound bite that has been re-cut so that it has a different meaning." So a word might be dropped in a sentence to force the plot move along. The only part of the show not spliced and diced? The emotional rose ceremonies.
The Top 10 Most Dramatic 'Bachelor' Finales
We take a look at the franchise's biggest shockers.
There is allegedly a psychologist available to the contestants 24/7 to help them unload the emotional stress while filming. In addition, each contestant is also supposed to meet with a private investigator so producers can stay ahead of any controversial or tabloid-y stories that might be lurking in one's closet.
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Producers tracked the contestants' menstrual cycles to interview them at optimal emotional moments for heightened reactions. Former Bachelor/Bachelorette producer, Ben Hatta, said, "So a girl's now crying, mid-interview, about nothing, or being reactionary to things that are super-small. It helped the producers, because now you've got someone who is emotional—and all you want is emotion."

redpajama_set_name: RedPajamaCommonCrawl


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text:
National Youth Self Portrait Prize
NYSPP 2009
Previous exhibition
from Wednesday 22 July until Sunday 13 September 2009
An annual event, the National Youth Self Portrait Prize seeks to encourage young people to embrace self portraiture and its expressive possibilities. Sponsored by ADFAS and the Tallis Foundation, a $10000 prize is offered for the most outstanding self portrait. The winner of the 2009 National Youth Self Portrait Prize is Hugh Marchant.
Hugh, 2009 by Hugh Marchant
Jessica Herrington won the inaugural National Youth Self Portrait Prize in 2008 with a work, Awkward self, that dwelt upon the difficulty we have revealing ourselves. 'The work has something to do with social acceptance and people's need to feel comfortable', she explained. 'It comes from those awkward moments that everyone encounters but so readily discards and tries not to recognise.' Herrington has received a great deal of attention since being awarded the $10,000 prize, sponsored by the Tallis Foundation and the Association of Australian Decorative and Fine Arts Societies. 'There has been an incredible amount of exposure for my work, invitations to exhibit in both group and solo shows, and I am now represented by a gallery for emerging artists in Sydney. The prize helped fund my studies at the Australian National University and start my career as an artist.'
The artists' approaches in this year's National Youth Self Portrait Prize demonstrate a maturing since last year. Aged 18 to 25 they depict not so much of their nervy, anxious selves but more introspective and contemplative attitudes, suggesting that their collective state of mind is one of acceptance rather than struggle. The effort now is towards solving or beginning the search for remedy rather than surrendering to despair.
For the works on paper and canvas, the influence of study and the awareness of historical styles are notable. So too is acknowledging the genre's long history by referencing past artists' self portraits–although it is hard to know if this is a statement of familiarity and understanding or an homage to a favourite artist.
Illustration, cartoon or using plain text are not new forms of self portraiture, but do stand out amongst the more traditional media. In the hands of artists born in the late 1980s and early 1990s these forms allow them to take a more whimsical or even absurdist approach to representing themselves.
There is a marked contrast between the staged and the real in this year's photomedia entries. Artists have either set themselves in a fantasy or chosen the natural world as their milieu. The controlled photographic studio portrait allows for a complete story to be told, whereas the spontaneous or accidental rendering is likely to contain superfluous elements, which must be embraced or 'photoshopped out'. More self portraits are nude this year and especially striking is the assured female sexuality, which stands in contrast to more diffident male artists' approaches.
The artists' statements also reveal the thoughts and feelings behind the submissions. 'Fear of rejection and judgment from others is pervasive', explains one artist; 'The twenty-first century is inundated with distortion and an apprehension towards the future condition of mankind', writes another. The concern about being typecast as 'Generation Y' is common, as is being worthy of respect.
Time-based media artist Hugh Marchant, currently studying at the College of Fine Arts at the University of New South Wales, has submitted an unusual moving image with sound. Influenced by the American experimental sound artist Alvin Lucier, Marchant's self portrait has been created by re-filming footage played on a television screen, transforming his original image into what he describes as 'a fingerprint or a zebra pattern' of an indistinct face. A selection of entries in the National Youth Self Portrait Prize 2009 – whether pushing the boundaries of new media art or traditional works created using oil and canvas – will be on display at the Gallery from 23 July to 13 September 2009, with the winner announced at the opening of the exhibition.
Helena Bezzina
15 portraits
1 Wet Awakening, 2009 by Hind Habib. 2 Holga Diaries, 2009 by Lucia Fischer. 3 Self Portrait as King Abdullah, 2009 by Abdul Abdullah.
At the end of 2007 the National Portrait Gallery launched the inaugural National Youth Self Portrait Prize and artists aged between eighteen and twenty-five were invited to submit self portraits using a variety of media including drawing, painting, printmaking and traditional or digital photography.
An annual event, the National Youth Self Portrait Prize seeks to encourage young people to embrace self portraiture and its expressive possibilities.
Idle hours is an exhibition of luxurious beauty. Paintings, prints and drawings represent subjects in quiet moods and situations arranged according to the time of day they depict - reading, drawing, snoozing, bathing, sewing, gardening, sitting, looking, making love and spending tranquil time with companions. Works in the exhibition range from the beginning of the twentieth century to the present.


To some, vocoders unfortunately carry a reputation for the somewhat cheesy robot voice they've come to be associated with, but the fact is these versatile and underused processors are hardly a one-trick pony. In this article, we won't take too deep a dive into all the many uses for it, but we will take a look at how using the built-in noise source of Ableton's vocoder can be used as a creative layer in drum sound design.
One of the great properties of the vocoder is that it inherently functions as an envelope follower. This allows you to easily create matching layers for dynamic signals with very little work at all. Hi-hats are a particularly good candidate to layer with noise because they're mostly just rhythmic white noise themselves. I'll add some hi-hats to my set to get to started - for this demonstration, I'm using a loop from my Hi-Hat Pack which features recordings of acoustic hats played by various drummers.
As you can hear in the example (click the play button in the bottom left of the gif), real hi-hats typically have a lot more midrange than electronic samples. I want to remove some of that frequency range and the vocoder will help me soften the sound while preserving the dynamics and groove of the acoustic drum loop. So I'll go ahead and add Live's stock vocoder and make sure the carrier is set to "noise". The XY pad below the carrier selector is great for changing the texture of the white noise, but I found only a minor tweak to this was needed in this case as I'm after the traditional white noise sound to blend with my hats.
Now it's just a matter of playing around with the settings on the device while listening at 100% wet. For hats, I'm looking for a brighter sound, so I know I'm going to immediately pull the formants knob up so the vocoder focuses more on the upper frequencies in the noise. I know I'm going to want the vocoded sound to be tight with the original loop as well, so I turn up the depth to around 140% to better match the hats' amplitude envelope. I'll also go ahead and set the release to something close to the release on the hats I'm working with as well - a good starting place would be anywhere from 80ms to 200ms, but it will vary based on the original signal.
At this point, I'll switch my focus to the heart of the vocoder - the filter bank. A lot of this will be trial and error, but for these hats I liked the sound of fewer bands and also made the filters wider (using BW) and switched them to the "retro" setting. It's also a good idea to play around with the range - this can make a dramatic difference in the unpleasant washy phase sound you might be hearing. Finally, I start drawing over the bands looking for the best balance. For hats, my focus is on attenuating the upper range that can be rather harsh.
Finally, I pull back on the D/W knob to get a good mix of the original signal back in. The vocoder noise is rather quiet, so something around 75% should do. Now that I'm hearing the final result, I'll usually go back through the settings and tweak them, especially matching the release time so the noise doesn't bleed past the hats.
Snares are another good candidate for noise layers because that's essentially the sound snare wire produces. Envelope-shaped white noise even takes the place of a snare in some classic drum machines, so adding a layer with the vocoder will help add some of this feel to any snare sample. I'm starting with a simple loop that has its components isolated on individual tracks.
After placing the vocoder on the snare's track, I start adjusting the dials again. I think the noise could be just a little brighter, so I make a very minor adjustment to the formant dial. The real focus, however, is on the release, which I'll turn to around 500-600ms to add a nice decay to the noise.
After pulling back the D/W to add some of the original sound back in, this is the result. It has the feel of a vintage drum machine and adds some much needed brightness and decay to the snare sample. You can hear the difference in the above audio example as the snare switches between dry and vocoded each bar.
If you try loading a vocoder on a drum like this, you'll notice it sounds pretty awful. This is because the heavy bass doesn't translate well to the white noise and by default the release time is quite long. I know that I want this synthesized click sound to be very tight, so I'll bring the release down to just 50ms and raise the depth quite high as well right away. Then I'll make the usual small adjustment to formant, and modify the noise source to be a little less bright.
The biggest strides are made in the filter bank, as usual. I'll set to fewer bands again, but the critical step is in adjusting the frequency range. The lower frequency should be set around 300-350hz so the bass is ignored by the vocoder. Because there is so little information in the mids and highs of a bass drum - and particularly in this muffled one - the vocoded click is quite quiet, so I boost it using the gain control as well. From there, it's the usual balancing by drawing across the filters.
The audio example shows the difference after the synthesized click has been added with the vocoder, as it switches between dry and wet each bar. The kick itself not only becomes more defined, you can tell that this will cut through a dense mix better as well.


Creditbit price after a new low below 0.00050BTC against the Bitcoin found support and moved higher.
A major bearish trend line with resistance at 0.00050BTC on the 7-day chart of CRBIT/BTC (data feed from Coinmarketcap) was broken.
It performed better against the US Dollar, and managed to bounce from $0.70 to $1.00.
Creditbit price looks like found support against the US Dollar and Bitcoin, as there was a break above a major resistance in CRBIT/BTC.
In the last analysis, we saw an extension of a major decline Creditbit price towards 0.00050BTC against the Bitcoin. Later, the price broke the stated support area and traded as low as 0.00045BTC where it found support. After forming a base the price moved higher, and broke the 0.00050BTC resistance. Similarly, there was a dip in Creditbit price against the US Dollar towards $0.70. Later, the price formed a support and recovered sharply above the $0.90 level to trade near $1.00.
It looks like the price is recovering, as a major bearish trend line with resistance at 0.00050BTC on the 7-day chart of CRBIT/BTC was broken. The price also broke the 23.6% Fib retracement level of the last decline from the 0.00075BTC high to 0.00045BTC low. However, the price is struggling to gain momentum above 0.00060BTC.
It looks like it is finding hard to settle above the 50% Fib retracement level of the last decline from the 0.00075BTC high to 0.00045BTC low. However, there are signs of a solid recovery, and now if 0.00045BTC continues to hold, the price may rise. The next stop on the upside could be near 0.00070BTC, which is a crucial pivot.
Hourly MACD – The MACD for the CRBIT/BTC pair recovered a bit, but with no major momentum.
Hourly RSI – The RSI for the CRBIT/BTC pair is struggling to move above the 50 level.