Binary Sparse Coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization

We propose a sparse coding approach to address the problem of source-sensor localization and speech reconstruction. This approach relies on designing a dictionary of spatialized signals by projecting the microphone array recordings into the array manifolds characterized for different locations in a reverberant enclosure using the image model. Sparse representation over this dictionary enables identifying the subspace of the actual recordings and its correspondence to the source and sensor locations. The speech signal is reconstructed by inverse filtering the acoustic channels associated to the array manifolds. We provide rigorous analysis on the optimality of speech reconstruction by elucidating the links between inverse filtering and source separation followed by deconvolution. This procedure is evaluated for localization, reconstruction and recognition of simultaneous speech sources using real data recordings. The results demonstrate the effectiveness of the proposed approach and compare favorably against beamforming and independent component analysis techniques

Single channel overlapped-speech detection and separation of spontaneous conversations

PhD ThesisIn the thesis, spontaneous conversation containing both speech mixture and speech dialogue is considered. The speech mixture refers to speakers speaking simultaneously (i.e. the overlapped-speech). The speech dialogue refers to only one speaker is actively speaking and the other is silent. That Input conversation is firstly processed by the overlapped-speech detection. Two output signals are then segregated into dialogue and mixture formats. The dialogue is processed by speaker diarization. Its outputs are the individual speech of each speaker. The mixture is processed by speech separation. Its outputs are independent separated speech signals of the speaker. When the separation input contains only the mixture, blind speech separation approach is used. When the separation is assisted by the outputs of the speaker diarization, it is informed speech separation. The research presents novel: overlapped-speech detection algorithm, and two speech separation algorithms. The proposed overlapped-speech detection is an algorithm to estimate the switching instants of the input. Optimization loop is adapted to adopt the best capsulated audio features and to avoid the worst. The optimization depends on principles of the pattern recognition, and k-means clustering. For of 300 simulated conversations, averages of: False-Alarm Error is 1.9%, Missed-Speech Error is 0.4%, and Overlap-Speaker Error is 1%. Approximately, these errors equal the errors of best recent reliable speaker diarization corpuses. The proposed blind speech separation algorithm consists of four sequential techniques: filter-bank analysis, Non-negative Matrix Factorization (NMF), speaker clustering and filter-bank synthesis. Instead of the required speaker segmentation, effective standard framing is contributed. Average obtained objective tests (SAR, SDR and SIR) of 51 simulated conversations are: 5.06dB, 4.87dB and 12.47dB respectively. For the proposed informed speech separation algorithm, outputs of the speaker diarization are a generated-database. The database associated the speech separation by creating virtual targeted-speech and mixture. The contributed virtual signals are trained to facilitate the separation by homogenising them with the NMF-matrix elements of the real mixture. Contributed masking optimized the resulting speech. Average obtained SAR, SDR and SIR of 341 simulated conversations are 9.55dB, 1.12dB, and 2.97dB respectively. Per the objective tests of the two speech separation algorithms, they are in the mid-range of the well-known NMF-based audio and speech separation methods

Multimodal Data Fusion: An Overview of Methods, Challenges and Prospects

International audienceIn various disciplines, information about the same phenomenon can be acquired from different types of detectors, at different conditions, in multiple experiments or subjects, among others. We use the term "modality" for each such acquisition framework. Due to the rich characteristics of natural phenomena, it is rare that a single modality provides complete knowledge of the phenomenon of interest. The increasing availability of several modalities reporting on the same system introduces new degrees of freedom, which raise questions beyond those related to exploiting each modality separately. As we argue, many of these questions, or "challenges" , are common to multiple domains. This paper deals with two key questions: "why we need data fusion" and "how we perform it". The first question is motivated by numerous examples in science and technology, followed by a mathematical framework that showcases some of the benefits that data fusion provides. In order to address the second question, "diversity" is introduced as a key concept, and a number of data-driven solutions based on matrix and tensor decompositions are discussed, emphasizing how they account for diversity across the datasets. The aim of this paper is to provide the reader, regardless of his or her community of origin, with a taste of the vastness of the field, the prospects and opportunities that it holds

Blind audio-visual localization and separation via low-rank and sparsity

The ability to localize visual objects that are associated with an audio source and at the same time to separate the audio signal is a cornerstone in audio-visual signal-processing applications. However, available methods mainly focus on localizing only the visual objects, without audio separation abilities. Besides that, these methods often rely on either laborious preprocessing steps to segment video frames into semantic regions, or additional supervisions to guide their localization. In this paper, we aim to address the problem of visual source localization and audio separation in an unsupervised manner and avoid all preprocessing or post-processing steps. To this end, we devise a novel structured matrix decomposition method that decomposes the data matrix of each modality as a superposition of three terms: 1) a low-rank matrix capturing the background information; 2) a sparse matrix capturing the correlated components among the two modalities and, hence, uncovering the sound source in visual modality and the associated sound in audio modality; and 3) a third sparse matrix accounting for uncorrelated components, such as distracting objects in visual modality and irrelevant sound in audio modality. The generality of the proposed method is demonstrated by applying it onto three applications, namely: 1) visual localization of a sound source; 2) visually assisted audio separation; and 3) active speaker detection. Experimental results indicate the effectiveness of the proposed method on these application domains

Generative modeling of sequential data

In this thesis, we investigate various approaches for generative modeling, with a special emphasis on sequential data. Namely, we develop methodologies to deal with issues regarding representation (modeling choices), learning paradigm (e.g. maximum likelihood, method of moments, adversarial training), and optimization. For the representation aspect, we make the following contributions: -We argue that using a multi-modal latent representation (unlike popular methods such as variational autoencoders or generative adversarial networks) significantly enhances the generative model learning performance, as evidenced by the experiments we conduct on handwritten digit dataset (MNIST) and celebrity faces dataset (CELEB-A). -We prove that the standard factorial Hidden Markov model defined in the literature is not statistically identifiable. We propose two alternative identifiable models, and show their validity on unsupervised source separation examples. -We experimentally show that using a convolutional neural network architecture provides performance boost over time agnostic methods such as non-negative matrix factorization, and auto-encoders. -We experimentally show that using a recurrent neural network with a diagonal recurrent matrix increases the convergence speed and final accuracy of the model in most cases in a symbolic music modeling task. For the learning paradigm aspect, we make the following contributions: -We propose a method of moment based parameter learning framework for Hidden Markov Models (HMMs) with special transition structures such as mixture of HMMs, switching HMMs and HMMs with mixture emissions. -We propose a new generative model learning method which does approximate maximum likelihood parameter estimation for implicit generative models. -We argue that using an implicit generative model for audio source separation increases the performance over models which specify a cost function, such as NMF or autoencoders trained via maximum likelihood. We show performance improvement in speech mixtures created from the TIMIT dataset. For the optimization aspect, we make the following contributions: -We show that using the method of moment framework we propose in this thesis boosts the model performance when used as an initialization scheme for the expectation maximization algorithm. -We propose new optimization algorithms for identifiable alternatives to Factorial HMM. -We propose a two-step optimization algorithm for learning implicit generative models which efficiently learns multi-modal latent representations

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

Trennung und Schätzung der Anzahl von Audiosignalquellen mit Zeit- und Frequenzüberlappung

Everyday audio recordings involve mixture signals: music contains a mixture of instruments; in a meeting or conference, there is a mixture of human voices. For these mixtures, automatically separating or estimating the number of sources is a challenging task. A common assumption when processing mixtures in the time-frequency domain is that sources are not fully overlapped. However, in this work we consider some cases where the overlap is severe — for instance, when instruments play the same note (unison) or when many people speak concurrently ("cocktail party") — highlighting the need for new representations and more powerful models. To address the problems of source separation and count estimation, we use conventional signal processing techniques as well as deep neural networks (DNN). We first address the source separation problem for unison instrument mixtures, studying the distinct spectro-temporal modulations caused by vibrato. To exploit these modulations, we developed a method based on time warping, informed by an estimate of the fundamental frequency. For cases where such estimates are not available, we present an unsupervised model, inspired by the way humans group time-varying sources (common fate). This contribution comes with a novel representation that improves separation for overlapped and modulated sources on unison mixtures but also improves vocal and accompaniment separation when used as an input for a DNN model. Then, we focus on estimating the number of sources in a mixture, which is important for real-world scenarios. Our work on count estimation was motivated by a study on how humans can address this task, which lead us to conduct listening experiments, confirming that humans are only able to estimate the number of up to four sources correctly. To answer the question of whether machines can perform similarly, we present a DNN architecture, trained to estimate the number of concurrent speakers. Our results show improvements compared to other methods, and the model even outperformed humans on the same task. In both the source separation and source count estimation tasks, the key contribution of this thesis is the concept of “modulation”, which is important to computationally mimic human performance. Our proposed Common Fate Transform is an adequate representation to disentangle overlapping signals for separation, and an inspection of our DNN count estimation model revealed that it proceeds to find modulation-like intermediate features.Im Alltag sind wir von gemischten Signalen umgeben: Musik besteht aus einer Mischung von Instrumenten; in einem Meeting oder auf einer Konferenz sind wir einer Mischung menschlicher Stimmen ausgesetzt. Für diese Mischungen ist die automatische Quellentrennung oder die Bestimmung der Anzahl an Quellen eine anspruchsvolle Aufgabe. Eine häufige Annahme bei der Verarbeitung von gemischten Signalen im Zeit-Frequenzbereich ist, dass die Quellen sich nicht vollständig überlappen. In dieser Arbeit betrachten wir jedoch einige Fälle, in denen die Überlappung immens ist zum Beispiel, wenn Instrumente den gleichen Ton spielen (unisono) oder wenn viele Menschen gleichzeitig sprechen (Cocktailparty) —, so dass neue Signal-Repräsentationen und leistungsfähigere Modelle notwendig sind. Um die zwei genannten Probleme zu bewältigen, verwenden wir sowohl konventionelle Signalverbeitungsmethoden als auch tiefgehende neuronale Netze (DNN). Wir gehen zunächst auf das Problem der Quellentrennung für Unisono-Instrumentenmischungen ein und untersuchen die speziellen, durch Vibrato ausgelösten, zeitlich-spektralen Modulationen. Um diese Modulationen auszunutzen entwickelten wir eine Methode, die auf Zeitverzerrung basiert und eine Schätzung der Grundfrequenz als zusätzliche Information nutzt. Für Fälle, in denen diese Schätzungen nicht verfügbar sind, stellen wir ein unüberwachtes Modell vor, das inspiriert ist von der Art und Weise, wie Menschen zeitveränderliche Quellen gruppieren (Common Fate). Dieser Beitrag enthält eine neuartige Repräsentation, die die Separierbarkeit für überlappte und modulierte Quellen in Unisono-Mischungen erhöht, aber auch die Trennung in Gesang und Begleitung verbessert, wenn sie in einem DNN-Modell verwendet wird. Im Weiteren beschäftigen wir uns mit der Schätzung der Anzahl von Quellen in einer Mischung, was für reale Szenarien wichtig ist. Unsere Arbeit an der Schätzung der Anzahl war motiviert durch eine Studie, die zeigt, wie wir Menschen diese Aufgabe angehen. Dies hat uns dazu veranlasst, eigene Hörexperimente durchzuführen, die bestätigten, dass Menschen nur in der Lage sind, die Anzahl von bis zu vier Quellen korrekt abzuschätzen. Um nun die Frage zu beantworten, ob Maschinen dies ähnlich gut können, stellen wir eine DNN-Architektur vor, die erlernt hat, die Anzahl der gleichzeitig sprechenden Sprecher zu ermitteln. Die Ergebnisse zeigen Verbesserungen im Vergleich zu anderen Methoden, aber vor allem auch im Vergleich zu menschlichen Hörern. Sowohl bei der Quellentrennung als auch bei der Schätzung der Anzahl an Quellen ist ein Kernbeitrag dieser Arbeit das Konzept der “Modulation”, welches wichtig ist, um die Strategien von Menschen mittels Computern nachzuahmen. Unsere vorgeschlagene Common Fate Transformation ist eine adäquate Darstellung, um die Überlappung von Signalen für die Trennung zugänglich zu machen und eine Inspektion unseres DNN-Zählmodells ergab schließlich, dass sich auch hier modulationsähnliche Merkmale finden lassen