Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression

This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on monaural segregation. The method starts with a training stage that establishes a locally-linear Gaussian regression model between the directional coordinates of all the sources and the auditory features extracted from binaural measurements. While fixed-length wide-spectrum sounds (white noise) are used for training to reliably estimate the model parameters, we show that the testing (localization) can be extended to variable-length sparse-spectrum sounds (such as speech), thus enabling a wide range of realistic applications. Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces. We release a novel corpus of real-room recordings that allow quantitative evaluation of the co-localization method in the presence of one or two sound sources. Experiments demonstrate increased accuracy and speed relative to several state-of-the-art methods.Comment: 15 pages, 8 figure

Biologically-inspired auditory artificial intelligence for speech recognition in multi-talker environments

Understanding speech in the presence of distracting talkers is a difficult computational problem known as the cocktail party problem. Motivated by auditory processing in the human brain, this thesis developed a neural network to isolate the speech of a single talker given binaural input containing a target talker and multiple distractors. In this research the network is called a Binaural Speaker Isolation FFTNet or BSINet for short. To compare the performance of BSINet to human participant performance on recognizing the target talker's speech with a varying number of distractors, a "cocktail party" dataset was designed and made available online. This dataset also enables the comparison of network performance to human participant performance. Using the Word-Error-Rate metric for evaluation, this research finds that BSINet performs comparably to the human participants. Thus BSINet provides significant advancement for solving the challenging cocktail party problem.The research was funded by an NSERC Canada Discovery Grant, a Government of Alberta Centre for Autonomous Systems in Strengthening Future Communities grant, a MITACS Globalink Award, a NSERC CGS-M Award, and a AITF Graduate Student Scholarship

Move2Hear : active audio-visual source separation

We introduce the active audio-visual source separation problem, where an agent must move intelligently in order to better isolate the sounds coming from an object of interest in its environment. The agent hears multiple audio sources simultaneously (e.g., a person speaking down the hall in a noisy household) and must use its eyes and ears to automatically separate out the sounds originating from the target object within a limited time budget. Towards this goal, we introduce a reinforcement learning approach that trains movement policies controlling the agent’s camera and microphone placement over time, guided by the improvement in predicted audio separation quality. We demonstrate our approach in scenarios motivated by both augmented reality (system is already co-located with the target object) and mobile robotics (agent begins arbitrarily far from the target object). Using state-of-the-art realistic audio-visual simulations in 3D environments, we demonstrate our model’s ability to find minimal movement sequences with maximal payoff for audio source separation.Computer Science

Adaptive time-frequency analysis for cognitive source separation

This thesis introduces a framework for separating two speech sources in non-ideal, reverberant environments. The source separation architecture tries to mimic the extraordinary abilities of the human auditory system when performing source separation. A movable human dummy head residing in a normal office room is used to model the conditions humans experience when listening to complex auditory scenes. This thesis first investigates how the orthogonality of speech sources in the time-frequency domain drops with different reverberation times of the environment and shows that separation schemes based on ideal binary time-frequency-masks are suitable to perform source separation also under humanoid reverberant conditions. Prior to separating the sources, the movable human dummy head analyzes the auditory scene and estimates the positions of the sources and the fundamental frequency tracks. The source localization is implemented using an iterative approach based on the interaural time differences between the two ears and achieves a localization blur of less than three degrees in the azimuth plane. The source separation architecture implemented in this thesis extracts the orthogonal timefrequency points of the speech mixtures. It combines the positive features of the STFT with the positive features of the cochleagram representation. The overall goal of the source separation is to find the ideal STFT-mask. The core source separation process however is based on the analysis of the corresponding region in an additionally computed cochleagram, which shows more reliable Interaural Time Difference (ITD) estimations that are used for separation. Several algorithms based on the ITD and the fundamental frequency of the target source are evaluated for their source separation capabilities. To enhance the separation capabilities of the single algorithms, the results of the different algorithms are combined to compute a final estimate. In this way SIR gains of approximately 30 dB for two source scenarios are achieved. For three source scenarios SIR gains of up to 16 dB are attained. Compared to the standard binaural signal processing approaches like DUET and Fixed Beamforming the presented approach achieves up to 29 dB SIR gain.Diese Dissertation beschreibt ein Framework zur Separation zweier Quellen in nicht-idealen, echobehafteten Umgebungen. Die Architektur zur Quellenseparation orientiert sich dabei an den außergewöhnlichen Separationsfähigkeiten des menschlichen Gehörs. Um die Bedingungen eines Menschen in einer komplexen auditiven Szene zu imitieren, wird ein beweglicher, menschlicher Kunstkopf genutzt, der sich in einem üblichen Büroraum befindet. In einem ersten Schritt analysiert diese Dissertation, inwiefern die Orthogonalität von Sprachsignalen im Zeit-Frequenz-Bereich mit unterschiedlichen Nachhallzeiten abnimmt. Trotz der Orthogonalitätsabnahme sind Separationsansätze basierend auf idealen binären Masken geeignet um eine Trennung von Sprachsignalen auch unter menschlichen, echobehafteten Bedingungen zu realisieren. Bevor die Quellen getrennt werden, analysiert der bewegliche Kunstkopf die auditive Szene und schätzt die Positionen der einzelnen Quellen und den Verlauf der Grundfrequenz der Sprecher ab. Die Quellenlokalisation wird durch einen iterativen Ansatz basierend auf den Zeitunterschieden zwischen beiden Ohren verwirklicht und erreicht eine Lokalisierungsgenauigkeit von weniger als drei Grad in der Azimuth-Ebene. Die Quellenseparationsarchitektur die in dieser Arbeit implementiert wird, extrahiert die orthogonalen Zeit-Frequenz-Punkte der Sprachmixturen. Dazu werden die positiven Eigenschaften der STFT mit den positiven Eigenschaften des Cochleagrams kombiniert. Ziel ist es, die ideale STFT-Maske zu finden. Die eigentliche Quellentrennung basiert jedoch auf der Analyse der entsprechenden Region eines zusätzlich berechneten Cochleagrams. Auf diese Weise wird eine weitaus verlässlichere Auswertung der Zeitunterschiede zwischen den beiden Ohren verwirklicht. Mehrere Algorithmen basierend auf den interauralen Zeitunterschieden und der Grundfrequenz der Zielquelle werden bezüglich ihrer Separationsfähigkeiten evaluiert. Um die Trennungsmöglichkeiten der einzelnen Algorithmen zu erhöhen, werden die einzelnen Ergebnisse miteinander verknüpft um eine finale Abschätzung zu gewinnen. Auf diese Weise können SIR Gewinne von ungefähr 30 dB für Szenarien mit zwei Quellen erzielt werden. Für Szenarien mit drei Quellen werden Gewinne von bis zu 16 dB erzielt. Verglichen mit binauralen Standardverfahren zur Quellentrennung wie DUET oder Fixed Beamforming, gewinnt der vorgestellte Ansatz bis zu 29 dB SIR

Audio-Motor Integration for Robot Audition

International audienceIn the context of robotics, audio signal processing in the wild amounts to dealing with sounds recorded by a system that moves and whose actuators produce noise. This creates additional challenges in sound source localization, signal enhancement and recognition. But the speci-ficity of such platforms also brings interesting opportunities: can information about the robot actuators' states be meaningfully integrated in the audio processing pipeline to improve performance and efficiency? While robot audition grew to become an established field, methods that explicitly use motor-state information as a complementary modality to audio are scarcer. This chapter proposes a unified view of this endeavour, referred to as audio-motor integration. A literature review and two learning-based methods for audio-motor integration in robot audition are presented, with application to single-microphone sound source localization and ego-noise reduction on real data

Binaural Source Separation with Convolutional Neural Networks

This work is a study on source separation techniques for binaural music mixtures. The chosen framework uses a Convolutional Neural Network (CNN) to estimate time-frequency soft masks. This masks are used to extract the different sources from the original two-channel mixture signal. Its baseline single-channel architecture performed state-of-the-art results on monaural music mixtures under low-latency conditions. It has been extended to perform separation in two-channel signals, being the first two-channel CNN joint estimation architecture. This means that filters are learned for each source by taking in account both channels information. Furthermore, a specific binaural condition is included during training stage. It uses Interaural Level Difference (ILD) information to improve spatial images of extracted sources. Concurrently, we present a novel tool to create binaural scenes for testing purposes. Multiple binaural scenes are rendered from a music dataset of four instruments (voice, drums, bass and others). The CNN framework have been tested for these binaural scenes and compared with monaural and stereo results. The system showed a great amount of adaptability and good separation results in all the scenarios. These results are used to evaluate spatial information impact on separation performance

Sound Localization for Robot Navigation

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