. Mel Frequency Conversion Once the set of frequencies are obtained from FFT, – they can be converted into mels with the conversion function stated earlier, reproduced here: . Discrete Cosine Transform (DCT) The last step in finding the MFCCs of a – speech sample is performing a discrete cosine transform of the base-10 logarithm of the mels obtained in the step above. A discrete cosine transform is similar to a Fourier transform in that data is represented as a sum of sinusoids each with frequencies and amplitudes. In the case of DCTs, a set of finite, discrete points are represented as a sum of cosines with amplitudes. Though are many of DCT including inverse “types” functions, we will examine DCT-II, the most commonly used DCT. The DCT-II function is for all k = 0 to N-1. Although a sum of cosines is obtained from this function, only the amplitudes of each given frequency is necessary these amplitudes for the frequencies – compose the mel frequency cepstral coefficients. MFCC aren’t only useful in speaker recognition applications. Researchers have found that the use of MFCC in recognizing MP3 encoded music is shown to be very robust. Making use of this method are a collection of mobile smart phone that allow users to determine the artist, title, album name, or other piece of “apps” information about a song by having their phone to a sample of the music “listen” (holding it up to a stereo playing the song). (Trivial note: The word is actually derived by reversing the first four “cepstral” letters in “spectral”.) For the purposes of our senior design project, we will be using mel frequency cepstral coefficients as our feature selection method. Methods of Pattern Matching Feature selection occurs prior to pattern matching against a database of known values for the features chosen. Pattern matching can be considered the final step in speaker recognition (verification) if the decision process is included. We will
. Mel Frequency Conversion – Once the set of frequencies are obtained from FFT, they can be converted into mels with the conversion function stated earlier, reproduced here: . Discrete Cosine Transform (DCT) – The last step in finding the MFCCs of a speech sample is performing a discrete cosine transform of the base-10 logarithm of the mels obtained in the step above. A discrete cosine transform is similar to a Fourier transform in that data is represented as a sum of sinusoids each with frequencies and amplitudes. In the case of DCTs, a set of finite, discrete points are represented as a sum of cosines with amplitudes. Though are many “types” of DCT including inverse functions, we will examine DCT-II, the most commonly used DCT. The DCT-II function is for all k = 0 to N-1. Although a sum of cosines is obtained from this function, only the amplitudes of each given frequency is necessary – these amplitudes for the frequencies compose the mel frequency cepstral coefficients. MFCC aren’t only useful in speaker recognition applications. Researchers have found that the use of MFCC in recognizing MP3 encoded music is shown to be very robust. Making use of this method are a collection of mobile smart phone “apps” that allow users to determine the artist, title, album name, or other piece of information about a song by having their phone “listen” to a sample of the music (holding it up to a stereo playing the song). (Trivial note: The word “cepstral” is actually derived by reversing the first four letters in “spectral”.) For the purposes of our senior design project, we will be using mel frequency cepstral coefficients as our feature selection method. Methods of Pattern Matching Feature selection occurs prior to pattern matching against a database of known values for the features chosen. Pattern matching can be considered the final step in speaker recognition (verification) if the decision process is included. We will
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