Bag-of-words representations for computer audition

Computer audition is omnipresent in everyday life, in applications ranging from personalised virtual agents to health care. From a technical point of view, the goal is to robustly classify the content of an audio signal in terms of a defined set of labels, such as, e.g., the acoustic scene, a medical diagnosis, or, in the case of speech, what is said or how it is said. Typical approaches employ machine learning (ML), which means that task-specific models are trained by means of examples. Despite recent successes in neural network-based end-to-end learning, taking the raw audio signal as input, models relying on hand-crafted acoustic features are still superior in some domains, especially for tasks where data is scarce. One major issue is nevertheless that a sequence of acoustic low-level descriptors (LLDs) cannot be fed directly into many ML algorithms as they require a static and fixed-length input. Moreover, also for dynamic classifiers, compressing the information of the LLDs over a temporal block by summarising them can be beneficial. However, the type of instance-level representation has a fundamental impact on the performance of the model. In this thesis, the so-called bag-of-audio-words (BoAW) representation is investigated as an alternative to the standard approach of statistical functionals. BoAW is an unsupervised method of representation learning, inspired from the bag-of-words method in natural language processing, forming a histogram of the terms present in a document. The toolkit openXBOW is introduced, enabling systematic learning and optimisation of these feature representations, unified across arbitrary modalities of numeric or symbolic descriptors. A number of experiments on BoAW are presented and discussed, focussing on a large number of potential applications and corresponding databases, ranging from emotion recognition in speech to medical diagnosis. The evaluations include a comparison of different acoustic LLD sets and configurations of the BoAW generation process. The key findings are that BoAW features are a meaningful alternative to statistical functionals, offering certain benefits, while being able to preserve the advantages of functionals, such as data-independence. Furthermore, it is shown that both representations are complementary and their fusion improves the performance of a machine listening system.Maschinelles Hören ist im täglichen Leben allgegenwärtig, mit Anwendungen, die von personalisierten virtuellen Agenten bis hin zum Gesundheitswesen reichen. Aus technischer Sicht besteht das Ziel darin, den Inhalt eines Audiosignals hinsichtlich einer Auswahl definierter Labels robust zu klassifizieren. Die Labels beschreiben bspw. die akustische Umgebung der Aufnahme, eine medizinische Diagnose oder - im Falle von Sprache - was gesagt wird oder wie es gesagt wird. Übliche Ansätze hierzu verwenden maschinelles Lernen, d.h., es werden anwendungsspezifische Modelle anhand von Beispieldaten trainiert. Trotz jüngster Erfolge beim Ende-zu-Ende-Lernen mittels neuronaler Netze, in welchen das unverarbeitete Audiosignal als Eingabe benutzt wird, sind Modelle, die auf definierten akustischen Merkmalen basieren, in manchen Bereichen weiterhin überlegen. Dies gilt im Besonderen für Einsatzzwecke, für die nur wenige Daten vorhanden sind. Allerdings besteht dabei das Problem, dass Zeitfolgen von akustischen Deskriptoren in viele Algorithmen des maschinellen Lernens nicht direkt eingespeist werden können, da diese eine statische Eingabe fester Länge benötigen. Außerdem kann es auch für dynamische (zeitabhängige) Klassifikatoren vorteilhaft sein, die Deskriptoren über ein gewisses Zeitintervall zusammenzufassen. Jedoch hat die Art der Merkmalsdarstellung einen grundlegenden Einfluss auf die Leistungsfähigkeit des Modells. In der vorliegenden Dissertation wird der sogenannte Bag-of-Audio-Words-Ansatz (BoAW) als Alternative zum Standardansatz der statistischen Funktionale untersucht. BoAW ist eine Methode des unüberwachten Lernens von Merkmalsdarstellungen, die von der Bag-of-Words-Methode in der Computerlinguistik inspiriert wurde, bei der ein Textdokument als Histogramm der vorkommenden Wörter beschrieben wird. Das Toolkit openXBOW wird vorgestellt, welches systematisches Training und Optimierung dieser Merkmalsdarstellungen - vereinheitlicht für beliebige Modalitäten mit numerischen oder symbolischen Deskriptoren - erlaubt. Es werden einige Experimente zum BoAW-Ansatz durchgeführt und diskutiert, die sich auf eine große Zahl möglicher Anwendungen und entsprechende Datensätze beziehen, von der Emotionserkennung in gesprochener Sprache bis zur medizinischen Diagnostik. Die Auswertungen beinhalten einen Vergleich verschiedener akustischer Deskriptoren und Konfigurationen der BoAW-Methode. Die wichtigsten Erkenntnisse sind, dass BoAW-Merkmalsvektoren eine geeignete Alternative zu statistischen Funktionalen darstellen, gewisse Vorzüge bieten und gleichzeitig wichtige Eigenschaften der Funktionale, wie bspw. die Datenunabhängigkeit, erhalten können. Zudem wird gezeigt, dass beide Darstellungen komplementär sind und eine Fusionierung die Leistungsfähigkeit eines Systems des maschinellen Hörens verbessert

Classification of EMI discharge sources using time–frequency features and multi-class support vector machine

This paper introduces the first application of feature extraction and machine learning to Electromagnetic Interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves Time-Frequency image calculation of EMI signals using General Linear Chirplet Analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform Local Binary Patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using Multi-Class Support Vector Machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system

Speaker independent isolated word recognition

The work presented in this thesis concerns the recognition of isolated words using a pattern matching approach. In such a system, an unknown speech utterance, which is to be identified, is transformed into a pattern of characteristic features. These features are then compared with a set of pre-stored reference patterns that were generated from the vocabulary words. The unknown word is identified as that vocabulary word for which the reference pattern gives the best match. One of the major difficul ties in the pattern comparison process is that speech patterns, obtained from the same word, exhibit non-linear temporal fluctuations and thus a high degree of redundancy. The initial part of this thesis considers various dynamic time warping techniques used for normalizing the temporal differences between speech patterns. Redundancy removal methods are also considered, and their effect on the recognition accuracy is assessed. Although the use of dynamic time warping algorithms provide considerable improvement in the accuracy of isolated word recognition schemes, the performance is ultimately limited by their poor ability to discriminate between acoustically similar words. Methods for enhancing the identification rate among acoustically similar words, by using common pattern features for similar sounding regions, are investigated. Pattern matching based, speaker independent systems, can only operate with a high recognition rate, by using multiple reference patterns for each of the words included in the vocabulary. These patterns are obtained from the utterances of a group of speakers. The use of multiple reference patterns, not only leads to a large increase in the memory requirements of the recognizer, but also an increase in the computational load. A recognition system is proposed in this thesis, which overcomes these difficulties by (i) employing vector quantization techniques to reduce the storage of reference patterns, and (ii) eliminating the need for dynamic time warping which reduces the computational complexity of the system. Finally, a method of identifying the acoustic structure of an utterance in terms of voiced, unvoiced, and silence segments by using fuzzy set theory is proposed. The acoustic structure is then employed to enhance the recognition accuracy of a conventional isolated word recognizer

Identity verification using voice and its use in a privacy preserving system

Since security has been a growing concern in recent years, the field of biometrics has gained popularity and became an active research area. Beside new identity authentication and recognition methods, protection against theft of biometric data and potential privacy loss are current directions in biometric systems research. Biometric traits which are used for verification can be grouped into two: physical and behavioral traits. Physical traits such as fingerprints and iris patterns are characteristics that do not undergo major changes over time. On the other hand, behavioral traits such as voice, signature, and gait are more variable; they are therefore more suitable to lower security applications. Behavioral traits such as voice and signature also have the advantage of being able to generate numerous different biometric templates of the same modality (e.g. different pass-phrases or signatures), in order to provide cancelability of the biometric template and to prevent crossmatching of different databases. In this thesis, we present three new biometric verification systems based mainly on voice modality. First, we propose a text-dependent (TD) system where acoustic features are extracted from individual frames of the utterances, after they are aligned via phonetic HMMs. Data from 163 speakers from the TIDIGITS database are employed for this work and the best equal error rate (EER) is reported as 0.49% for 6-digit user passwords. Second, a text-independent (TI) speaker verification method is implemented inspired by the feature extraction method utilized for our text-dependent system. Our proposed TI system depends on creating speaker specific phoneme codebooks. Once phoneme codebooks are created on the enrollment stage using HMM alignment and segmentation to extract discriminative user information, test utterances are verified by calculating the total dissimilarity/distance to the claimed codebook. For benchmarking, a GMM-based TI system is implemented as a baseline. The results of the proposed TD system (0.22% EER for 7-digit passwords) is superior compared to the GMM-based system (0.31% EER for 7-digit sequences) whereas the proposed TI system yields worse results (5.79% EER for 7-digit sequences) using the data of 163 people from the TIDIGITS database . Finally, we introduce a new implementation of the multi-biometric template framework of Yanikoglu and Kholmatov [12], using fingerprint and voice modalities. In this framework, two biometric data are fused at the template level to create a multi-biometric template, in order to increase template security and privacy. The current work aims to also provide cancelability by exploiting the behavioral aspect of the voice modality

Composition of Deep and Spiking Neural Networks for Very Low Bit Rate Speech Coding

Most current very low bit rate (VLBR) speech coding systems use hidden Markov model (HMM) based speech recognition/synthesis techniques. This allows transmission of information (such as phonemes) segment by segment that decreases the bit rate. However, the encoder based on a phoneme speech recognition may create bursts of segmental errors. Segmental errors are further propagated to optional suprasegmental (such as syllable) information coding. Together with the errors of voicing detection in pitch parametrization, HMM-based speech coding creates speech discontinuities and unnatural speech sound artefacts. In this paper, we propose a novel VLBR speech coding framework based on neural networks (NNs) for end-to-end speech analysis and synthesis without HMMs. The speech coding framework relies on phonological (sub-phonetic) representation of speech, and it is designed as a composition of deep and spiking NNs: a bank of phonological analysers at the transmitter, and a phonological synthesizer at the receiver, both realised as deep NNs, and a spiking NN as an incremental and robust encoder of syllable boundaries for coding of continuous fundamental frequency (F0). A combination of phonological features defines much more sound patterns than phonetic features defined by HMM-based speech coders, and the finer analysis/synthesis code contributes into smoother encoded speech. Listeners significantly prefer the NN-based approach due to fewer discontinuities and speech artefacts of the encoded speech. A single forward pass is required during the speech encoding and decoding. The proposed VLBR speech coding operates at a bit rate of approximately 360 bits/s

A Study on Deep Learning: Training, Models and Applications

In the past few years, deep learning has become a very important research field that has attracted a lot of research interests, attributing to the development of the computational hardware like high performance GPUs, training deep models, such as fully-connected deep neural networks (DNNs) and convolutional neural networks (CNNs), from scratch becomes practical, and using well-trained deep models to deal with real-world large scale problems also becomes possible. This dissertation mainly focuses on three important problems in deep learning, i.e., training algorithm, computational models and applications, and provides several methods to improve the performances of different deep learning methods. The first method is a DNN training algorithm called Annealed Gradient Descent (AGD). This dissertation presents a theoretical analysis on the convergence properties and learning speed of AGD to show its benefits. Experimental results have shown that AGD yields comparable performance as SGD but it can significantly expedite training of DNNs in big data sets. Secondly, this dissertation proposes to apply a novel model, namely Hybrid Orthogonal Projection and Estimation (HOPE), to CNNs. HOPE can be viewed as a hybrid model to combine feature extraction with mixture models. The experimental results have shown that HOPE layers can significantly improve the performance of CNNs in the image classification tasks. The third proposed method is to apply CNNs to image saliency detection. In this approach, a gradient descent method is used to iteratively modify the input images based on pixel-wise gradients to reduce a pre-defined cost function. Moreover, SLIC superpixels and low level saliency features are applied to smooth and refine the saliency maps. Experimental results have shown that the proposed methods can generate high-quality salience maps. The last method is also for image saliency detection. However, this method is based on Generative Adversarial Network (GAN). Different from GAN, the proposed method uses fully supervised learning to learn G-Network and D-Network. Therefore, it is called Supervised Adversarial Network (SAN). Moreover, SAN introduces a different G-Network and conv-comparison layers to further improve the saliency performance. Experimental results show that the SAN model can also generate state-of-the-art saliency maps for complicate images

Machine learning techniques for music information retrieval

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2015The advent of digital music has changed the rules of music consumption, distribution and sales. With it has emerged the need to effectively search and manage vast music collections. Music information retrieval is an interdisciplinary field of research that focuses on the development of new techniques with that aim in mind. This dissertation addresses a specific aspect of this field: methods that automatically extract musical information exclusively based on the audio signal. We propose a method for automatic music-based classification, label inference, and music similarity estimation. Our method consist in representing the audio with a finite set of symbols and then modeling the symbols time evolution. The symbols are obtained via vector quantization in which a single codebook is used to quantize the audio descriptors. The symbols time evolution is modeled via a first order Markov process. Based on systematic evaluations we carried out on publicly available sets, we show that our method achieves performances on par with most techniques found in literature. We also present and discuss the problems that appear when computers try to classify or annotate songs using the audio as the only source of information. In our method, the separation of quantization process from the creation and training of classification models helped us in that analysis. It enabled us to examine how instantaneous sound attributes (henceforth features) are distributed in term of musical genre, and how designing codebooks specially tailored for these distributions affects the performance of ours and other classification systems commonly used for this task. On this issue, we show that there is no apparent benefit in seeking a thorough representation of the feature space. This is a bit unexpected since it goes against the assumption that features carry equally relevant information loads and somehow capture the specificities of musical facets, implicit in many genre recognition methods. Label inference is the task of automatically annotating songs with semantic words - this tasks is also known as autotagging. In this context, we illustrate the importance of a number of issues, that in our perspective, are often overlooked. We show that current techniques are fragile in the sense that small alterations in the set of labels may lead to dramatically different results. Furthermore, through a series of experiments, we show that autotagging systems fail to learn tag models capable to generalize to datasets of different origins. We also show that the performance achieved with these techniques is not sufficient to be able to take advantage of the correlations between tags.Fundação para a Ciência e a Tecnologia (FCT