Sound Object Recognition

Humans are constantly exposed to a variety of acoustic stimuli ranging from music and speech to more complex acoustic scenes like a noisy marketplace. The human auditory perception mechanism is able to analyze these different kinds of sounds and extract meaningful information suggesting that the same processing mechanism is capable of representing different sound classes. In this thesis, we test this hypothesis by proposing a high dimensional sound object representation framework, that captures the various modulations of sound by performing a multi-resolution mapping. We then show that this model is able to capture a wide variety of sound classes (speech, music, soundscapes) by applying it to the tasks of speech recognition, speaker verification, musical instrument recognition and acoustic soundscape recognition. We propose a multi-resolution analysis approach that captures the detailed variations in the spectral characterists as a basis for recognizing sound objects. We then show how such a system can be fine tuned to capture both the message information (speech content) and the messenger information (speaker identity). This system is shown to outperform state-of-art system for noise robustness at both automatic speech recognition and speaker verification tasks. The proposed analysis scheme with the included ability to analyze temporal modulations was used to capture musical sound objects. We showed that using a model of cortical processing, we were able to accurately replicate the human perceptual similarity judgments and also were able to get a good classification performance on a large set of musical instruments. We also show that neither just the spectral feature or the marginals of the proposed model are sufficient to capture human perception. Moreover, we were able to extend this model to continuous musical recordings by proposing a new method to extract notes from the recordings. Complex acoustic scenes like a sports stadium have multiple sources producing sounds at the same time. We show that the proposed representation scheme can not only capture these complex acoustic scenes, but provides a flexible mechanism to adapt to target sources of interest. The human auditory perception system is known to be a complex system where there are both bottom-up analysis pathways and top-down feedback mechanisms. The top-down feedback enhances the output of the bottom-up system to better realize the target sounds. In this thesis we propose an implementation of top-down attention module which is complimentary to the high dimensional acoustic feature extraction mechanism. This attention module is a distributed system operating at multiple stages of representation, effectively acting as a retuning mechanism, that adapts the same system to different tasks. We showed that such an adaptation mechanism is able to tremendously improve the performance of the system at detecting the target source in the presence of various distracting background sources

Phonetic Classification Using Hierarchical, Feed-forward, Spectro-temporal Patch-based Architectures

A preliminary set of experiments are described in which a biologically-inspired computer vision system (Serre, Wolf et al. 2005; Serre 2006; Serre, Oliva et al. 2006; Serre, Wolf et al. 2006) designed for visual object recognition was applied to the task of phonetic classification. During learning, the systemprocessed 2-D wideband magnitude spectrograms directly as images, producing a set of 2-D spectrotemporal patch dictionaries at different spectro-temporal positions, orientations, scales, and of varying complexity. During testing, features were computed by comparing the stored patches with patches fromnovel spectrograms. Classification was performed using a regularized least squares classifier (Rifkin, Yeo et al. 2003; Rifkin, Schutte et al. 2007) trained on the features computed by the system. On a 20-class TIMIT vowel classification task, the model features achieved a best result of 58.74% error, compared to 48.57% error using state-of-the-art MFCC-based features trained using the same classifier. This suggests that hierarchical, feed-forward, spectro-temporal patch-based architectures may be useful for phoneticanalysis

Denoising sound signals in a bioinspired non-negative spectro-temporal domain

The representation of sound signals at the cochlea and auditory cortical level has been studied as an alternative to classical analysis methods. In this work, we put forward a recently proposed feature extraction method called approximate auditory cortical representation, based on an approximation to the statistics of discharge patterns at the primary auditory cortex. The approach here proposed estimates a non-negative sparse coding with a combined dictionary of atoms. These atoms represent the spectro-temporal receptive fields of the auditory cortical neurons, and are calculated from the auditory spectrograms of clean signal and noise. The denoising is carried out on noisy signals by the reconstruction of the signal discarding the atoms corresponding to the noise. Experiments are presented using synthetic (chirps) and real data (speech), in the presence of additive noise. For the evaluation of the new method and its variants, we used two objective measures: the perceptual evaluation of speech quality and the segmental signal-to-noise ratio. Results show that the proposed method improves the quality of the signals, mainly under severe degradation.Fil: Martínez, César Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Goddard, J.. Universidad Autónoma Metropolitana; MéxicoFil: Di Persia, Leandro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentin

Representation of speech in the primary auditory cortex and its implications for robust speech processing

Speech has evolved as a primary form of communication between humans. This most used means of communication has been the subject of intense study for years, but there is still a lot that we do not know about it. It is an oft repeated fact, that even the performance of the best speech processing algorithms still lags far behind that of the average human, It seems inescapable that unless we know more about the way the brain performs this task, our machines can not go much further. This thesis focuses on the question of speech representation in the brain, both from a physiological and technological perspective. We explore the representation of speech through the encoding of its smallest elements - phonemic features - in the primary auditory cortex. We report on how population of neurons with diverse tuning properties respond discriminately to phonemes resulting in explicit encoding of their parameters. Next, we show that this sparse encoding of the phonemic features is a simple consequence of the linear spectro-temporal properties of the auditory cortical neurons and that a Spectro-Temporal receptive field model can predict similar patterns of activation. This is an important step toward the realization of systems that operate based on the same principles as the cortex. Using an inverse method of reconstruction, we shall also explore the extent to which phonemic features are preserved in the cortical representation of noisy speech. The results suggest that the cortical responses are more robust to noise and that the important features of phonemes are preserved in the cortical representation even in noise. Finally, we explain how a model of this cortical representation can be used for speech processing and enhancement applications to improve their robustness and performance

Computational Models of Representation and Plasticity in the Central Auditory System

The performance for automated speech processing tasks like speech recognition and speech activity detection rapidly degrades in challenging acoustic conditions. It is therefore necessary to engineer systems that extract meaningful information from sound while exhibiting invariance to background noise, different speakers, and other disruptive channel conditions. In this thesis, we take a biomimetic approach to these problems, and explore computational strategies used by the central auditory system that underlie neural information extraction from sound. In the first part of this thesis, we explore coding strategies employed by the central auditory system that yield neural responses that exhibit desirable noise robustness. We specifically demonstrate that a coding strategy based on sustained neural firings yields richly structured spectro-temporal receptive fields (STRFs) that reflect the structure and diversity of natural sounds. The emergent receptive fields are comparable to known physiological neuronal properties and can be employed as a signal processing strategy to improve noise invariance in a speech recognition task. Next, we extend the model of sound encoding based on spectro-temporal receptive fields to incorporate the cognitive effects of selective attention. We propose a framework for modeling attention-driven plasticity that induces changes to receptive fields driven by task demands. We define a discriminative cost function whose optimization and solution reflect a biologically plausible strategy for STRF adaptation that helps listeners better attend to target sounds. Importantly, the adaptation patterns predicted by the framework have a close correspondence with known neurophysiological data. We next generalize the framework to act on the spectro-temporal dynamics of task-relevant stimuli, and make predictions for tasks that have yet to be experimentally measured. We argue that our generalization represents a form of object-based attention, which helps shed light on the current debate about auditory attentional mechanisms. Finally, we show how attention-modulated STRFs form a high-fidelity representation of the attended target, and we apply our results to obtain improvements in a speech activity detection task. Overall, the results of this thesis improve our general understanding of central auditory processing, and our computational frameworks can be used to guide further studies in animal models. Furthermore, our models inspire signal processing strategies that are useful for automated speech and sound processing tasks

Using auditory classification images for the identification of fine acoustic cues used in speech perception

International audienceAn essential step in understanding the processes underlying the general mechanism of perceptual categorization is to identify which portions of a physical stimulation modulate the behavior of our perceptual system. More specifically, in the context of speech comprehension, it is still a major open challenge to understand which information is used to categorize a speech stimulus as one phoneme or another, the auditory primitives relevant for the categorical perception of speech being still unknown. Here we propose to adapt a method relying on a Generalized Linear Model with smoothness priors, already used in the visual domain for the estimation of so-called classification images, to auditory experiments. This statistical model offers a rigorous framework for dealing with non-Gaussian noise, as it is often the case in the auditory modality, and limits the amount of noise in the estimated template by enforcing smoother solutions. By applying this technique to a specific two-alternative forced choice experiment between stimuli " aba " and " ada " in noise with an adaptive SNR, we confirm that the second formantic transition is key for classifying phonemes into /b/ or /d/ in noise, and that its estimation by the auditory system is a relative measurement across spectral bands and in relation to the perceived height of the second formant in the preceding syllable. Through this example, we show how the GLM with smoothness priors approach can be applied to the identification of fine functional acoustic cues in speech perception. Finally we discuss some assumptions of the model in the specific case of speech perception

Understanding and Decoding Imagined Speech using Electrocorticographic Recordings in Humans

Certain brain disorders, resulting from brainstem infarcts, traumatic brain injury, stroke and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders would benefit from a system that can infer internal speech directly from brain signals. Investigating how the human cortex encodes imagined speech remains a difficult challenge, due to the lack of behavioral and observable measures. As a consequence, the fine temporal properties of speech cannot be synchronized precisely with brain signals during internal subjective experiences, like imagined speech. This thesis aims at understanding and decoding the neural correlates of imagined speech (also called internal speech or covert speech), for targeting speech neuroprostheses. In this exploratory work, various imagined speech features, such as acoustic sound features, phonetic representations, and individual words were investigated and decoded from electrocorticographic signals recorded in epileptic patients in three different studies. This recording technique provides high spatiotemporal resolution, via electrodes placed beneath the skull, but without penetrating the cortex In the first study, we reconstructed continuous spectrotemporal acoustic features from brain signals recorded during imagined speech using cross-condition linear regression. Using this technique, we showed that significant acoustic features of imagined speech could be reconstructed in seven patients. In the second study, we decoded continuous phoneme sequences from brain signals recorded during imagined speech using hidden Markov models. This technique allowed incorporating a language model that defined phoneme transitions probabilities. In this preliminary study, decoding accuracy was significant across eight phonemes in one patients. In the third study, we classified individual words from brain signals recorded during an imagined speech word repetition task, using support-vector machines. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the classification framework. Classification accuracy was significant across five patients. In order to compare speech representations across conditions and integrate imagined speech into the general speech network, we investigated imagined speech in parallel with overt speech production and/or speech perception. Results shared across the three studies showed partial overlapping between imagined speech and speech perception/production in speech areas, such as superior temporal lobe, anterior frontal gyrus and sensorimotor cortex. In an attempt to understanding higher-level cognitive processing of auditory processes, we also investigated the neural encoding of acoustic features during music imagery using linear regression. Despite this study was not directly related to speech representations, it provided a unique opportunity to quantitatively study features of inner subjective experiences, similar to speech imagery. These studies demonstrated the potential of using predictive models for basic decoding of speech features. Despite low performance, results show the feasibility for direct decoding of natural speech. In this respect, we highlighted numerous challenges that were encountered, and suggested new avenues to improve performances

On the Combination of Auditory and Modulation Frequency Channels for ASR applications

This paper investigates the combination of evidence coming from different frequency channels obtained filtering the speech signal at different auditory and modulation frequencies. In our previous work \cite{icassp2008}, we showed that combination of classifiers trained on different ranges of {\it modulation} frequencies is more effective if performed in sequential (hierarchical) fashion. In this work we verify that combination of classifiers trained on different ranges of {\it auditory} frequencies is more effective if performed in parallel fashion. Furthermore we propose an architecture based on neural networks for combining evidence coming from different auditory-modulation frequency sub-bands that takes advantages of previous findings. This reduces the final WER by 6.2\% (from 45.8\% to 39.6\%) w.r.t the single classifier approach in a LVCSR task