w2v-SELD: A Sound Event Localization and Detection Framework for Self-Supervised Spatial Audio Pre-Training

Sound Event Detection and Localization (SELD) constitutes a complex task that depends on extensive multichannel audio recordings with annotated sound events and their respective locations. In this paper, we introduce a self-supervised approach for SELD adapted from the pre-training methodology of wav2vec 2.0, which learns representations directly from raw audio data, eliminating the need for supervision. By applying this approach to SELD, we can leverage a substantial amount of unlabeled 3D audio data to learn robust representations of sound events and their locations. Our method comprises two primary stages: pre-training and fine-tuning. In the pre-training phase, unlabeled 3D audio datasets are utilized to train our w2v-SELD model, capturing intricate high-level features and contextual information inherent in audio signals. Subsequently, in the fine-tuning stage, a smaller dataset with labeled SELD data fine-tunes the pre-trained model. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed self-supervised approach for SELD. The model surpasses baseline systems provided with the datasets and achieves competitive performance comparable to state-of-the-art supervised methods. The code and pre-trained parameters of our w2v-SELD model are available in this repository.Comment: 17 pages, 5 figure

Apprentissage automatique pour le codage cognitif de la parole

Depuis les années 80, les codecs vocaux reposent sur des stratégies de codage à court terme qui fonctionnent au niveau de la sous-trame ou de la trame (généralement 5 à 20 ms). Les chercheurs ont essentiellement ajusté et combiné un nombre limité de technologies disponibles (transformation, prédiction linéaire, quantification) et de stratégies (suivi de forme d'onde, mise en forme du bruit) pour construire des architectures de codage de plus en plus complexes. Dans cette thèse, plutôt que de s'appuyer sur des stratégies de codage à court terme, nous développons un cadre alternatif pour la compression de la parole en codant les attributs de la parole qui sont des caractéristiques perceptuellement importantes des signaux vocaux. Afin d'atteindre cet objectif, nous résolvons trois problèmes de complexité croissante, à savoir la classification, la prédiction et l'apprentissage des représentations. La classification est un élément courant dans les conceptions de codecs modernes. Dans un premier temps, nous concevons un classifieur pour identifier les émotions, qui sont parmi les attributs à long terme les plus complexes de la parole. Dans une deuxième étape, nous concevons un prédicteur d'échantillon de parole, qui est un autre élément commun dans les conceptions de codecs modernes, pour mettre en évidence les avantages du traitement du signal de parole à long terme et non linéaire. Ensuite, nous explorons les variables latentes, un espace de représentations de la parole, pour coder les attributs de la parole à court et à long terme. Enfin, nous proposons un réseau décodeur pour synthétiser les signaux de parole à partir de ces représentations, ce qui constitue notre dernière étape vers la construction d'une méthode complète de compression de la parole basée sur l'apprentissage automatique de bout en bout. Bien que chaque étape de développement proposée dans cette thèse puisse faire partie d'un codec à elle seule, chaque étape fournit également des informations et une base pour la prochaine étape de développement jusqu'à ce qu'un codec entièrement basé sur l'apprentissage automatique soit atteint. Les deux premières étapes, la classification et la prédiction, fournissent de nouveaux outils qui pourraient remplacer et améliorer des éléments des codecs existants. Dans la première étape, nous utilisons une combinaison de modèle source-filtre et de machine à état liquide (LSM), pour démontrer que les caractéristiques liées aux émotions peuvent être facilement extraites et classées à l'aide d'un simple classificateur. Dans la deuxième étape, un seul réseau de bout en bout utilisant une longue mémoire à court terme (LSTM) est utilisé pour produire des trames vocales avec une qualité subjective élevée pour les applications de masquage de perte de paquets (PLC). Dans les dernières étapes, nous nous appuyons sur les résultats des étapes précédentes pour concevoir un codec entièrement basé sur l'apprentissage automatique. un réseau d'encodage, formulé à l'aide d'un réseau neuronal profond (DNN) et entraîné sur plusieurs bases de données publiques, extrait et encode les représentations de la parole en utilisant la prédiction dans un espace latent. Une approche d'apprentissage non supervisé basée sur plusieurs principes de cognition est proposée pour extraire des représentations à partir de trames de parole courtes et longues en utilisant l'information mutuelle et la perte contrastive. La capacité de ces représentations apprises à capturer divers attributs de la parole à court et à long terme est démontrée. Enfin, une structure de décodage est proposée pour synthétiser des signaux de parole à partir de ces représentations. L'entraînement contradictoire est utilisé comme une approximation des mesures subjectives de la qualité de la parole afin de synthétiser des échantillons de parole à consonance naturelle. La haute qualité perceptuelle de la parole synthétisée ainsi obtenue prouve que les représentations extraites sont efficaces pour préserver toutes sortes d'attributs de la parole et donc qu'une méthode de compression complète est démontrée avec l'approche proposée.Abstract: Since the 80s, speech codecs have relied on short-term coding strategies that operate at the subframe or frame level (typically 5 to 20ms). Researchers essentially adjusted and combined a limited number of available technologies (transform, linear prediction, quantization) and strategies (waveform matching, noise shaping) to build increasingly complex coding architectures. In this thesis, rather than relying on short-term coding strategies, we develop an alternative framework for speech compression by encoding speech attributes that are perceptually important characteristics of speech signals. In order to achieve this objective, we solve three problems of increasing complexity, namely classification, prediction and representation learning. Classification is a common element in modern codec designs. In a first step, we design a classifier to identify emotions, which are among the most complex long-term speech attributes. In a second step, we design a speech sample predictor, which is another common element in modern codec designs, to highlight the benefits of long-term and non-linear speech signal processing. Then, we explore latent variables, a space of speech representations, to encode both short-term and long-term speech attributes. Lastly, we propose a decoder network to synthesize speech signals from these representations, which constitutes our final step towards building a complete, end-to-end machine-learning based speech compression method. The first two steps, classification and prediction, provide new tools that could replace and improve elements of existing codecs. In the first step, we use a combination of source-filter model and liquid state machine (LSM), to demonstrate that features related to emotions can be easily extracted and classified using a simple classifier. In the second step, a single end-to-end network using long short-term memory (LSTM) is shown to produce speech frames with high subjective quality for packet loss concealment (PLC) applications. In the last steps, we build upon the results of previous steps to design a fully machine learning-based codec. An encoder network, formulated using a deep neural network (DNN) and trained on multiple public databases, extracts and encodes speech representations using prediction in a latent space. An unsupervised learning approach based on several principles of cognition is proposed to extract representations from both short and long frames of data using mutual information and contrastive loss. The ability of these learned representations to capture various short- and long-term speech attributes is demonstrated. Finally, a decoder structure is proposed to synthesize speech signals from these representations. Adversarial training is used as an approximation to subjective speech quality measures in order to synthesize natural-sounding speech samples. The high perceptual quality of synthesized speech thus achieved proves that the extracted representations are efficient at preserving all sorts of speech attributes and therefore that a complete compression method is demonstrated with the proposed approach

Deep Neural Networks for Sound Event Detection

The objective of this thesis is to develop novel classification and feature learning techniques for the task of sound event detection (SED) in real-world environments. Throughout their lives, humans experience a consistent learning process on how to assign meanings to sounds. Thanks to this, most of the humans can easily recognize the sound of a thunder, dog bark, door bell, bird singing etc. In this work, we aim to develop systems that can automatically detect the sound events commonly present in our daily lives. Such systems can be utilized in e.g. contextaware devices, acoustic surveillance, bio-acoustical and healthcare monitoring, and smart-home cities.In this thesis, we propose to apply the modern machine learning methods called deep learning for SED. The relationship between the commonly used timefrequency representations for SED (such as mel spectrogram and magnitude spectrogram) and the target sound event labels are highly complex. Deep learning methods such as deep neural networks (DNN) utilize a layered structure of units to extract features from the given sound representation input with increased abstraction at each layer. This increases the network’s capacity to efficiently learn the highly complex relationship between the sound representation and the target sound event labels. We found that the proposed DNN approach performs significantly better than the established classifier techniques for SED such as Gaussian mixture models.In a time-frequency representation of an audio recording, a sound event can often be recognized as a distinct pattern that may exhibit shifts in both dimensions. The intra-class variability of the sound events may cause to small shifts in the frequency domain content, and the time domain shift results from the fact that a sound event can occur at any time for a given audio recording. We found that convolutional neural networks (CNN) are useful to learn shift-invariant filters that are essential for robust modeling of sound events. In addition, we show that recurrent neural networks (RNN) are effective in modeling the long-term temporal characteristics of the sound events. Finally, we combine the convolutional and recurrent layers in a single classifier called convolutional recurrent neural networks (CRNN), which emphasizes the benefits of both and provides state-of-the-art results in multiple SED benchmark datasets.Aside from learning the mappings between the time-frequency representations and the sound event labels, we show that deep learning methods can also be utilized to learn a direct mapping between the the target labels and a lower level representation such as the magnitude spectrogram or even the raw audio signals. In this thesis, the feature learning capabilities of the deep learning methods and the empirical knowledge on the human auditory perception are proposed to be integrated through the means of layer weight initialization with filterbank coefficients. This results with an optimal, ad-hoc filterbank that is obtained through gradient based optimization of the original coefficients to improve the SED performance

Deep spiking neural networks with applications to human gesture recognition

The spiking neural networks (SNNs), as the 3rd generation of Artificial Neural Networks (ANNs), are a class of event-driven neuromorphic algorithms that potentially have a wide range of application domains and are applicable to a variety of extremely low power neuromorphic hardware. The work presented in this thesis addresses the challenges of human gesture recognition using novel SNN algorithms. It discusses the design of these algorithms for both visual and auditory domain human gesture recognition as well as event-based pre-processing toolkits for audio signals. From the visual gesture recognition aspect, a novel SNN-based event-driven hand gesture recognition system is proposed. This system is shown to be effective in an experiment on hand gesture recognition with its spiking recurrent convolutional neural network (SCRNN) design, which combines both designed convolution operation and recurrent connectivity to maintain spatial and temporal relations with address-event-representation (AER) data. The proposed SCRNN architecture can achieve arbitrary temporal resolution, which means it can exploit temporal correlations between event collections. This design utilises a backpropagation-based training algorithm and does not suffer from gradient vanishing/explosion problems. From the audio perspective, a novel end-to-end spiking speech emotion recognition system (SER) is proposed. This system employs the MFCC as its main speech feature extractor as well as a self-designed latency coding algorithm to effciently convert the raw signal to AER input that can be used for SNN. A two-layer spiking recurrent architecture is proposed to address temporal correlations between spike trains. The robustness of this system is supported by several open public datasets, which demonstrate state of the arts recognition accuracy and a significant reduction in network size, computational costs, and training speed. In addition to directly contributing to neuromorphic SER, this thesis proposes a novel speech-coding algorithm based on the working mechanism of humans auditory organ system. The algorithm mimics the functionality of the cochlea and successfully provides an alternative method of event-data acquisition for audio-based data. The algorithm is then further simplified and extended into an application of speech enhancement which is jointly used in the proposed SER system. This speech-enhancement method uses the lateral inhibition mechanism as a frequency coincidence detector to remove uncorrelated noise in the time-frequency spectrum. The method is shown to be effective by experiments for up to six types of noise.The spiking neural networks (SNNs), as the 3rd generation of Artificial Neural Networks (ANNs), are a class of event-driven neuromorphic algorithms that potentially have a wide range of application domains and are applicable to a variety of extremely low power neuromorphic hardware. The work presented in this thesis addresses the challenges of human gesture recognition using novel SNN algorithms. It discusses the design of these algorithms for both visual and auditory domain human gesture recognition as well as event-based pre-processing toolkits for audio signals. From the visual gesture recognition aspect, a novel SNN-based event-driven hand gesture recognition system is proposed. This system is shown to be effective in an experiment on hand gesture recognition with its spiking recurrent convolutional neural network (SCRNN) design, which combines both designed convolution operation and recurrent connectivity to maintain spatial and temporal relations with address-event-representation (AER) data. The proposed SCRNN architecture can achieve arbitrary temporal resolution, which means it can exploit temporal correlations between event collections. This design utilises a backpropagation-based training algorithm and does not suffer from gradient vanishing/explosion problems. From the audio perspective, a novel end-to-end spiking speech emotion recognition system (SER) is proposed. This system employs the MFCC as its main speech feature extractor as well as a self-designed latency coding algorithm to effciently convert the raw signal to AER input that can be used for SNN. A two-layer spiking recurrent architecture is proposed to address temporal correlations between spike trains. The robustness of this system is supported by several open public datasets, which demonstrate state of the arts recognition accuracy and a significant reduction in network size, computational costs, and training speed. In addition to directly contributing to neuromorphic SER, this thesis proposes a novel speech-coding algorithm based on the working mechanism of humans auditory organ system. The algorithm mimics the functionality of the cochlea and successfully provides an alternative method of event-data acquisition for audio-based data. The algorithm is then further simplified and extended into an application of speech enhancement which is jointly used in the proposed SER system. This speech-enhancement method uses the lateral inhibition mechanism as a frequency coincidence detector to remove uncorrelated noise in the time-frequency spectrum. The method is shown to be effective by experiments for up to six types of noise

Intelligent Control of Dynamic Range Compressor

PhD ThesisMusic production is an essential element in the value chain of modern music. It includes enhancing the recorded audio tracks, balancing the loudness level of multiple tracks as well as making artistic decisions to satisfy music genre, style and emotion. Similarly to related professions in creative media production, the tools for music making are now highly computerised. However, many parts of the work remain labour intensive and time consuming. The demand for intelligent tools is therefore growing. This situation encourages the emerging trend of ever increasing research into intelligent music production tools. Since audio effects are among the main tools used by music producers, there are many discussions and developments targeting the controlling mechanism of audio effects. This thesis is aiming at pushing the boundaries in this field by investigating the intelligent control of one of the essential audio effects, the dynamic range compressor. This research presents an innovative control system design. The core of this design is to learn from a reference audio, and control the dynamic range compressor to make the processed input audio sounds as close as possible to the reference. One of the proposed approaches can be divided into three stages, a feature extractor, a trained regression model, and an objective evaluation algorithm. In the feature extractor stage we firstly test feature sets using conventional audio features commonly used in speech and audio signal analyses. Substantially, we test handcrafted audio features specifically designed to characterise audio properties related to the dynamic range of audio samples. Research into feature design has been completed at different levels of complexity. A series of feature selection schemes are also assessed to select the optimal feature sets from both conventional and specifically designed audio features. In the subsequent stage of the research, feature extraction is replaced by a feature learning deep neural network (DNN). This is addressing the problem that the previous features are exclusive to each parameter, while a general feature extractor may be formed using DNN. A universal feature extractor can reduce the computational cost and become easier to adapt to more complex audio materials as well. The second stage of the control system is a trained regression model. Random forest regression is selected from several algorithms using experimental validation. Since different feature extractors are tested with increasingly complex audio material, as well as exclusive to the DRC’s parameters, e.g., attack time or compression ratio, separate models are trained and tested respectively. The third component of our approach is a method for evaluation. A computational audio similarity algorithm was designed to verify the results using auditory models. This algorithm is based on estimating the distance between two statistical models fitted on perceptually motivated audio features characterising similarity in loudness and timbre. Finally, the overall system is evaluated with both objective and subjective methods. The main contribution of this Thesis is a method for using a reference audio to control a dynamic range compressor. Besides the system design, the analysis of the evaluation provides useful insights of the relations between audio effects and audio features as well as auditory perception. The research is conducted in a way that it is possible to transfer the knowledge to other audio effects and other use case scenarios, providing an alternative research direction in the field of intelligent music production and simplifying how audio effects are controlled for end users.

Learning Feature Representation for Automatic Speech Recognition

Feature extraction in automatic speech recognition (ASR) can be regarded as learning representations from lower-level to more abstract higher-level features. Lower-level feature can be viewed as features from the signal domain, such as perceptual linear predictive (PLP) and Mel-frequency cepstral coefficients (MFCCs) features. Higher-level feature representations can be considered as bottleneck features (BNFs) learned using deep neural networks (DNNs). In this thesis, we focus on improving feature extraction at different levels mainly for ASR. The first part of this thesis focuses on learning features from the signal domain that help ASR. Hand-crafted spectral and cepstral features such as MFCC are the main features used in most conventional ASR systems; all are inspired by physiological models of the human auditory system. However, some aspects of the signal such as pitch cannot be easily extracted from spectral features, but are found to be useful for ASR. We explore new algorithm to extract a pitch feature directly from a signal for ASR and show that this feature, appended to the other feature, gives consistent improvements in various languages, especially tonal languages. We then investigate replacing the conventional features with jointly training from the signal domain using time domain, and frequency domain approaches. The results show that our time-domain joint feature learning setup achieves state-of-the-art performance using MFCC, while our frequency domain setup outperforms them in various datasets. Joint feature extraction results in learning data or language-dependent filter banks, that can degrade the performance in unseen noise and channel conditions or other languages. To tackle this, we investigate joint universal feature learning across different languages using the proposed direct-from-signal setups. We then investigate the filter banks learned in this setup and propose a new set of features as an extension to conventional Mel filter banks. The results show consistent word error rate (WER) improvement, especially in clean condition. The second part of this thesis focuses on learning higher-level feature embedding. We investigate learning and transferring deep feature representations across different domains using multi-task learning and weight transfer approaches. They have been adopted to explicitly learn intermediate-level features that are useful for several different tasks