Analysis of very low quality speech for mask-based enhancement

The complexity of the speech enhancement problem has motivated many different solutions. However, most techniques address situations in which the target speech is fully intelligible and the background noise energy is low in comparison with that of the speech. Thus while current enhancement algorithms can improve the perceived quality, the intelligibility of the speech is not increased significantly and may even be reduced. Recent research shows that intelligibility of very noisy speech can be improved by the use of a binary mask, in which a binary weight is applied to each time-frequency bin of the input spectrogram. There are several alternative goals for the binary mask estimator, based either on the Signal-to-Noise Ratio (SNR) of each time-frequency bin or on the speech signal characteristics alone. Our approach to the binary mask estimation problem aims to preserve the important speech cues independently of the noise present by identifying time-frequency regions that contain significant speech energy. The speech power spectrum varies greatly for different types of speech sound. The energy of voiced speech sounds is concentrated in the harmonics of the fundamental frequency while that of unvoiced sounds is, in contrast, distributed across a broad range of frequencies. To identify the presence of speech energy in a noisy speech signal we have therefore developed two detection algorithms. The first is a robust algorithm that identifies voiced speech segments and estimates their fundamental frequency. The second detects the presence of sibilants and estimates their energy distribution. In addition, we have developed a robust algorithm to estimate the active level of the speech. The outputs of these algorithms are combined with other features estimated from the noisy speech to form the input to a classifier which estimates a mask that accurately reflects the time-frequency distribution of speech energy even at low SNR levels. We evaluate a mask-based speech enhancer on a range of speech and noise signals and demonstrate a consistent increase in an objective intelligibility measure with respect to noisy speech.Open Acces

Machine learning and inferencing for the decomposition of speech mixtures

In this dissertation, we present and evaluate a novel approach for incorporating machine learning and inferencing into the time-frequency decomposition of speech signals in the context of speaker-independent multi-speaker pitch tracking. The pitch tracking performance of the resulting algorithm is comparable to that of a state-of-the-art machine-learning algorithm for multi-pitch tracking while being significantly more computationally efficient and requiring much less training data. Multi-pitch tracking is a time-frequency signal processing problem in which mutual interferences of the harmonics from different speakers make it challenging to design an algorithm to reliably estimate the fundamental frequency trajectories of the individual speakers. The current state-of-the-art in speaker-independent multi-pitch tracking utilizes 1) a deep neural network for producing spectrograms of individual speakers and 2) another deep neural network that acts upon the individual spectrograms and the original audio’s spectrogram to produce estimates of the pitch tracks of the individual speakers. However, the implementation of this Multi-Spectrogram Machine- Learning (MS-ML) algorithm could be computationally intensive and make it impractical for hardware platforms such as embedded devices where the computational power is limited. Instead of utilizing deep neural networks to estimate the pitch values directly, we have derived and evaluated a fault recognition and diagnosis (FRD) framework that utilizes machine learning and inferencing techniques to recognize potential faults in the pitch tracks produced by a traditional multi-pitch tracking algorithm. The result of this fault-recognition phase is then used to trigger a fault-diagnosis phase aimed at resolving the recognized fault(s) through adaptive adjustment of the time-frequency analysis of the input signal. The pitch estimates produced by the resulting FRD-ML algorithm are found to be comparable in accuracy to those produced via the MS-ML algorithm. However, our evaluation of the FRD-ML algorithm shows it to have significant advantages over the MS-ML algorithm. Specifically, the number of multiplications per second in FRD-ML is found to be two orders of magnitude less while the number of additions per second is about the same as in the MS-ML algorithm. Furthermore, the required amount of training data to achieve optimal performance is found to be two orders of magnitude less for the FRD-ML algorithm in comparison to the MS-ML algorithm. The reduction in the number of multiplications per second means it is more feasible to implement the MPT solution on hardware platforms with limited computational power such as embedded devices rather than relying on Graphics Processing Units (GPUs) or cloud computing. The reduction in training data size makes the algorithm more flexible in terms of configuring for different application scenarios such as training for different languages where there may not be a large amount of training data

Binaural scene analysis : localization, detection and recognition of speakers in complex acoustic scenes

The human auditory system has the striking ability to robustly localize and recognize a specific target source in complex acoustic environments while ignoring interfering sources. Surprisingly, this remarkable capability, which is referred to as auditory scene analysis, is achieved by only analyzing the waveforms reaching the two ears. Computers, however, are presently not able to compete with the performance achieved by the human auditory system, even in the restricted paradigm of confronting a computer algorithm based on binaural signals with a highly constrained version of auditory scene analysis, such as localizing a sound source in a reverberant environment or recognizing a speaker in the presence of interfering noise. In particular, the problem of focusing on an individual speech source in the presence of competing speakers, termed the cocktail party problem, has been proven to be extremely challenging for computer algorithms. The primary objective of this thesis is the development of a binaural scene analyzer that is able to jointly localize, detect and recognize multiple speech sources in the presence of reverberation and interfering noise. The processing of the proposed system is divided into three main stages: localization stage, detection of speech sources, and recognition of speaker identities. The only information that is assumed to be known a priori is the number of target speech sources that are present in the acoustic mixture. Furthermore, the aim of this work is to reduce the performance gap between humans and machines by improving the performance of the individual building blocks of the binaural scene analyzer. First, a binaural front-end inspired by auditory processing is designed to robustly determine the azimuth of multiple, simultaneously active sound sources in the presence of reverberation. The localization model builds on the supervised learning of azimuthdependent binaural cues, namely interaural time and level differences. Multi-conditional training is performed to incorporate the uncertainty of these binaural cues resulting from reverberation and the presence of competing sound sources. Second, a speech detection module that exploits the distinct spectral characteristics of speech and noise signals is developed to automatically select azimuthal positions that are likely to correspond to speech sources. Due to the established link between the localization stage and the recognition stage, which is realized by the speech detection module, the proposed binaural scene analyzer is able to selectively focus on a predefined number of speech sources that are positioned at unknown spatial locations, while ignoring interfering noise sources emerging from other spatial directions. Third, the speaker identities of all detected speech sources are recognized in the final stage of the model. To reduce the impact of environmental noise on the speaker recognition performance, a missing data classifier is combined with the adaptation of speaker models using a universal background model. This combination is particularly beneficial in nonstationary background noise

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

A psychoacoustic engineering approach to machine sound source separation in reverberant environments

Reverberation continues to present a major problem for sound source separation algorithms, due to its corruption of many of the acoustical cues on which these algorithms rely. However, humans demonstrate a remarkable robustness to reverberation and many psychophysical and perceptual mechanisms are well documented. This thesis therefore considers the research question: can the reverberation–performance of existing psychoacoustic engineering approaches to machine source separation be improved? The precedence effect is a perceptual mechanism that aids our ability to localise sounds in reverberant environments. Despite this, relatively little work has been done on incorporating the precedence effect into automated sound source separation. Consequently, a study was conducted that compared several computational precedence models and their impact on the performance of a baseline separation algorithm. The algorithm included a precedence model, which was replaced with the other precedence models during the investigation. The models were tested using a novel metric in a range of reverberant rooms and with a range of other mixture parameters. The metric, termed Ideal Binary Mask Ratio, is shown to be robust to the effects of reverberation and facilitates meaningful and direct comparison between algorithms across different acoustic conditions. Large differences between the performances of the models were observed. The results showed that a separation algorithm incorporating a model based on interaural coherence produces the greatest performance gain over the baseline algorithm. The results from the study also indicated that it may be necessary to adapt the precedence model to the acoustic conditions in which the model is utilised. This effect is analogous to the perceptual Clifton effect, which is a dynamic component of the precedence effect that appears to adapt precedence to a given acoustic environment in order to maximise its effectiveness. However, no work has been carried out on adapting a precedence model to the acoustic conditions under test. Specifically, although the necessity for such a component has been suggested in the literature, neither its necessity nor benefit has been formally validated. Consequently, a further study was conducted in which parameters of each of the previously compared precedence models were varied in each room in order to identify if, and to what extent, the separation performance varied with these parameters. The results showed that the reverberation–performance of existing psychoacoustic engineering approaches to machine source separation can be improved and can yield significant gains in separation performance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A User-assisted Approach to Multiple Instrument Music Transcription

PhDThe task of automatic music transcription has been studied for several decades and is regarded as an enabling technology for a multitude of applications such as music retrieval and discovery, intelligent music processing and large-scale musicological analyses. It refers to the process of identifying the musical content of a performance and representing it in a symbolic format. Despite its long research history, fully automatic music transcription systems are still error prone and often fail when more complex polyphonic music is analysed. This gives rise to the question in what ways human knowledge can be incorporated in the transcription process. This thesis investigates ways to involve a human user in the transcription process. More specifically, it is investigated how user input can be employed to derive timbre models for the instruments in a music recording, which are employed to obtain instrument-specific (parts-based) transcriptions. A first investigation studies different types of user input in order to derive instrument models by means of a non-negative matrix factorisation framework. The transcription accuracy of the different models is evaluated and a method is proposed that refines the models by allowing each pitch of each instrument to be represented by multiple basis functions. A second study aims at limiting the amount of user input to make the method more applicable in practice. Different methods are considered to estimate missing non-negative basis functions when only a subset of basis functions can be extracted based on the user information. A method is proposed to track the pitches of individual instruments over time by means of a Viterbi framework in which the states at each time frame contain several candidate instrument-pitch combinations. A transition probability is employed that combines three different criteria: the frame-wise reconstruction error of each combination, a pitch continuity measure that favours similar pitches in consecutive frames, and an explicit activity model for each instrument. The method is shown to outperform other state-of-the-art multi-instrument tracking methods. Finally, the extraction of instrument models that include phase information is investigated as a step towards complex matrix decomposition. The phase relations between the partials of harmonic sounds are explored as a time-invariant property that can be employed to form complex-valued basis functions. The application of the model for a user-assisted transcription task is illustrated with a saxophone example.QMU