BIOACOUSTIC SEGMENTATION BY HIERARCHICAL DIRICHLET PROCESS HIDDEN MARKOV MODEL
Understanding the communication between different animals by analysing their acoustic signals is an important topic in bioacoustics. It can be a powerful tool for the preservation of ecological diversity. We investigate probabilistic models to analyse signals issued from real-world bioacoustic sound scenes. We study a Bayesian non-parametric sequential models based on Hierarchical Dirichlet Process Hidden Markov Models (HDP-HMM). The model is able to infer hidden states, that are referred here as song units. However, using such a model raise one main issue: defining the number of hidden states the model has to learn. In bioacoustic problems we often do not know the number of song units (unlike in human speech recognition). Hence, we work with the Hierarchical Dirichlet Process (HDP)-HMM, which is a Bayesian non-parametric (BNP) model that offers a way to tackle this challenging problem. We focus our work on unsupervised learning from bioacoustic data. It consists in simultaneously finding the structure of hidden song units and automatically infer the unknown number of the hidden states to represent the data. Two real bioacoustic sound scene applications are investigated in this work: on whale and multi-species birds segmentation. The learning of these models is proceeded by using Markov-Chain Monte Carlo (MCMC) sampling techniques on Mel Frequency Cepstral Coefficients (MFCC) of audio signals. The results show an interesting song unit segmentation of the bioacoustic signals and open new insights for unsupervised analysis of such signals. This paper illustrates the potential of chunking non-human animal signals into structured parts. This can yield to a new species representation and help experts to better understand the behaviour of such species as Kershenbaum et al. (2014) wanted.
This paper applies HDP-HMM to challenging bioacoustics segmentation problems including humpback whole sound and bird sound segmentation. Although the technique itself is not novel, the application of this data-driven method to bioacoustics segmentation is quite challenging, and may yield some scientific findings, and this is a valuable contribution to the bioacoustics field. My concern for this paper is that it does not have fair comparison of the other simple methods including BIC and AIC, and it is better to provide such comparisons. Especially, as the authors pointed out, the computational cost of HDP-HMM is a big issue, and the other simple methods may solve this issue.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
BIOACOUSTIC SEGMENTATION BY HIERARCHICAL DIRICHLET PROCESS HIDDEN MARKOV MODEL
Understanding the communication between different animals by analysing their acoustic signals is an important topic in bioacoustics. It can be a powerful tool for the preservation of ecological diversity. We investigate probabilistic models to analyse signals issued from real-world bioacoustic sound scenes. We study a Bayesian non-parametric sequential models based on Hierarchical Dirichlet Process Hidden Markov Models (HDP-HMM). The model is able to infer hidden states, that are referred here as song units. However, using such a model raise one main issue: defining the number of hidden states the model has to learn. In bioacoustic problems we often do not know the number of song units (unlike in human speech recognition). Hence, we work with the Hierarchical Dirichlet Process (HDP)-HMM, which is a Bayesian non-parametric (BNP) model that offers a way to tackle this challenging problem. We focus our work on unsupervised learning from bioacoustic data. It consists in simultaneously finding the structure of hidden song units and automatically infer the unknown number of the hidden states to represent the data. Two real bioacoustic sound scene applications are investigated in this work: on whale and multi-species birds segmentation. The learning of these models is proceeded by using Markov-Chain Monte Carlo (MCMC) sampling techniques on Mel Frequency Cepstral Coefficients (MFCC) of audio signals. The results show an interesting song unit segmentation of the bioacoustic signals and open new insights for unsupervised analysis of such signals. This paper illustrates the potential of chunking non-human animal signals into structured parts. This can yield to a new species representation and help experts to better understand the behaviour of such species as Kershenbaum et al. (2014) wanted.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper applies HDP-HMM to challenging bioacoustics segmentation problems including humpback whole sound and bird sound segmentation. Although the technique itself is not novel, the application of this data-driven method to bioacoustics segmentation is quite challenging, and may yield some scientific findings, and this is a valuable contribution to the bioacoustics field. My concern for this paper is that it does not have fair comparison of the other simple methods including BIC and AIC, and it is better to provide such comparisons. Especially, as the authors pointed out, the computational cost of HDP-HMM is a big issue, and the other simple methods may solve this issue.
assistant
This paper applies HDP-HMM to challenging bioacoustics segmentation problems including humpback whole sound and bird sound segmentation. Although the technique itself is not novel, the application of this data-driven method to bioacoustics segmentation is quite challenging, and may yield some scientific findings, and this is a valuable contribution to the bioacoustics field. My concern for this paper is that it does not have fair comparison of the other simple methods including BIC and AIC, and it is better to provide such comparisons. Especially, as the authors pointed out, the computational cost of HDP-HMM is a big issue, and the other simple methods may solve this issue.