Source Separation for Target Enhancement of Food Intake Acoustics from Noisy Recordings

International audienceAutomatic food intake monitoring can be significantly beneficial in the fight against obesity and weight management in our society today. Different sensing modalities have been used in several research efforts to accomplish automatic food intake monitoring with acoustic sensors being the most common. In this study, we explore the ability to learn spectral patterns of food intake acoustics from a clean signal and use this learned patterns for extracting the signal of interest from a noisy recording. Using standard metrics for evaluation of blind source separation, namely signal to distortion ratio and signal to interference ratio, we observed up to 20dB improvement of separation quality in very low signal to noise ratio conditions. For more practical performance evaluation of food intake monitoring, we compared the detection accuracy for chew events on the mixed/noisy signal versus on the estimated/separated target signal. We observed up to 60% improvement in chew event detection accuracy for low signal to noise ratio conditions when using the estimated target signal compared to when using the mixed/noisy signal. – Index Terms—food intake monitoring, audio source separation , nonnegative matrix factorization, harmonizable processe

I hear you eat and speak: automatic recognition of eating condition and food type, use-cases, and impact on ASR performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient

PROJET - Spatial Audio Separation Using Projections

International audienceWe propose a projection-based method for the unmixing of multi-channel audio signals into their different constituent spatial objects. Here, spatial objects are modelled using a unified framework which handles both point sources and diffuse sources. We then propose a novel methodology to estimate and take advantage of the spatial dependencies of an object. Where previous research has processed the original multichannel mixtures directly and has been principally focused on the use of inter-channel covariance structures, here we instead process projections of the multichannel signal on many different spatial directions. These linear combinations consist of observations where some spatial objects are cancelled or enhanced. We then propose an algorithm which takes these projections as the observations, discarding dependencies between them. Since each one contains global information regarding all channels of the original multichannel mixture, this provides an effective means of learning the parameters of the original audio, while avoiding the need for joint-processing of all the channels. We further show how to recover the separated spatial objects and demonstrate the use of the technique on stereophonic music signals

Scalable Source Localization with Multichannel Alpha-Stable Distributions

International audienceIn this paper, we focus on the problem of sound source localization and we propose a technique that exploits the known and arbitrary geometry of the microphone array. While most probabilistic techniques presented in the past rely on Gaussian models, we go further in this direction and detail a method for source localization that is based on the recently proposed alpha-stable harmonizable processes. They include Cauchy and Gaussian as special cases and their remarkable feature is to allow a simple modeling of impulsive and real world sounds with few parameters. The approach we present builds on the classical convolutive mixing model and has the particularities of requiring going through the data only once, to also work in the underdetermined case of more sources than microphones and to allow massively parallelizable implementations operating in the time-frequency domain. We show that the method yields interesting performance for acoustic imaging in realistic simulations

Quantization-aware Parameter Estimation for Audio Upmixing

International audienceUpmixing consists in extracting audio objects out of their downmix, given some parameters computed beforehand at a coding stage. It is an important task in audio processing with many applications in the entertainment industry. One particularly successful approach for this purpose is to compress the audio objects through nonnegative matrix factorization (NMF) parameters at the coder, to be used for separating the downmix at the decoder. In this paper, we focus on such NMF methods for audio compression, which operate at very low parameter bitrates. In existing methods, parameter estimation and quantization are conducted independently. Here, we propose two extensions: first, we jointly estimate and quantize the parameters at the coder to ensure good reconstruction at the decoder. Second, we propose a parameter refinement method operated at the decoder, that benefits from priors induced by quantization to yield better performance. We show that our contributions outperform existing baseline methods

Principled methods for mixtures processing

This document is my thesis for getting the habilitation à diriger des recherches, which is the french diploma that is required to fully supervise Ph.D. students. It summarizes the research I did in the last 15 years and also provides the short­term research directions and applications I want to investigate. Regarding my past research, I first describe the work I did on probabilistic audio modeling, including the separation of Gaussian and α­stable stochastic processes. Then, I mention my work on deep learning applied to audio, which rapidly turned into a large effort for community service. Finally, I present my contributions in machine learning, with some works on hardware compressed sensing and probabilistic generative models.My research programme involves a theoretical part that revolves around probabilistic machine learning, and an applied part that concerns the processing of time series arising in both audio and life sciences