456 research outputs found
Polyphonic Sound Event Detection by using Capsule Neural Networks
Artificial sound event detection (SED) has the aim to mimic the human ability
to perceive and understand what is happening in the surroundings. Nowadays,
Deep Learning offers valuable techniques for this goal such as Convolutional
Neural Networks (CNNs). The Capsule Neural Network (CapsNet) architecture has
been recently introduced in the image processing field with the intent to
overcome some of the known limitations of CNNs, specifically regarding the
scarce robustness to affine transformations (i.e., perspective, size,
orientation) and the detection of overlapped images. This motivated the authors
to employ CapsNets to deal with the polyphonic-SED task, in which multiple
sound events occur simultaneously. Specifically, we propose to exploit the
capsule units to represent a set of distinctive properties for each individual
sound event. Capsule units are connected through a so-called "dynamic routing"
that encourages learning part-whole relationships and improves the detection
performance in a polyphonic context. This paper reports extensive evaluations
carried out on three publicly available datasets, showing how the CapsNet-based
algorithm not only outperforms standard CNNs but also allows to achieve the
best results with respect to the state of the art algorithms
Classification of Overlapped Audio Events Based on AT, PLSA, and the Combination of Them
Audio event classification, as an important part of Computational Auditory Scene Analysis, has attracted much attention. Currently, the classification technology is mature enough to classify isolated audio events accurately, but for overlapped audio events, it performs much worse. While in real life, most audio documents would have certain percentage of overlaps, and so the overlap classification problem is an important part of audio classification. Nowadays, the work on overlapped audio event classification is still scarce, and most existing overlap classification systems can only recognize one audio event for an overlap. In this paper, in order to deal with overlaps, we innovatively introduce the author-topic (AT) model which was first proposed for text analysis into audio classification, and innovatively combine it with PLSA (Probabilistic Latent Semantic Analysis). We propose 4 systems, i.e. AT, PLSA, AT-PLSA and PLSA-AT, to classify overlaps. The 4 proposed systems have the ability to recognize two or more audio events for an overlap. The experimental results show that the 4 systems perform well in classifying overlapped audio events, whether it is the overlap in training set or the overlap out of training set. Also they perform well in classifying isolated audio events
Automatic transcription of polyphonic music exploiting temporal evolution
PhDAutomatic music transcription is the process of converting an audio recording
into a symbolic representation using musical notation. It has numerous applications
in music information retrieval, computational musicology, and the
creation of interactive systems. Even for expert musicians, transcribing polyphonic
pieces of music is not a trivial task, and while the problem of automatic
pitch estimation for monophonic signals is considered to be solved, the creation
of an automated system able to transcribe polyphonic music without setting
restrictions on the degree of polyphony and the instrument type still remains
open.
In this thesis, research on automatic transcription is performed by explicitly
incorporating information on the temporal evolution of sounds. First efforts address
the problem by focusing on signal processing techniques and by proposing
audio features utilising temporal characteristics. Techniques for note onset and
offset detection are also utilised for improving transcription performance. Subsequent
approaches propose transcription models based on shift-invariant probabilistic
latent component analysis (SI-PLCA), modeling the temporal evolution
of notes in a multiple-instrument case and supporting frequency modulations in
produced notes. Datasets and annotations for transcription research have also
been created during this work. Proposed systems have been privately as well as
publicly evaluated within the Music Information Retrieval Evaluation eXchange
(MIREX) framework. Proposed systems have been shown to outperform several
state-of-the-art transcription approaches.
Developed techniques have also been employed for other tasks related to music
technology, such as for key modulation detection, temperament estimation,
and automatic piano tutoring. Finally, proposed music transcription models
have also been utilized in a wider context, namely for modeling acoustic scenes
Trennung und Schätzung der Anzahl von Audiosignalquellen mit Zeit- und Frequenzßberlappung
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 â for instance, when instruments play the same note (unison) or when many people speak concurrently ("cocktail party") â 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 ďŹrst 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, conďŹrming 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 âmodulationâ, 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 ďŹnd 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. FĂźr diese Mischungen ist die automatische Quellentrennung oder die Bestimmung der Anzahl an Quellen eine anspruchsvolle Aufgabe. Eine häuďŹge Annahme bei der Verarbeitung von gemischten Signalen im Zeit-Frequenzbereich ist, dass die Quellen sich nicht vollständig Ăźberlappen. In dieser Arbeit betrachten wir jedoch einige Fälle, in denen die Ăberlappung immens ist zum Beispiel, wenn Instrumente den gleichen Ton spielen (unisono) oder wenn viele Menschen gleichzeitig sprechen (Cocktailparty) â, 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 fßr Unisono-Instrumentenmischungen ein und untersuchen die speziellen, durch Vibrato ausgelÜsten, zeitlich-spektralen Modulationen. Um diese Modulationen auszunutzen entwickelten wir eine Methode, die auf Zeitverzerrung basiert und eine Schätzung der Grundfrequenz als zusätzliche Information nutzt. Fßr Fälle, in denen diese Schätzungen nicht verfßgbar sind, stellen wir ein unßberwachtes 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 fßr ßberlappte und modulierte Quellen in Unisono-Mischungen erhÜht, 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 fßr 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 HÜrexperimente durchzufßhren, 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 kÜnnen, 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 HÜrern.
Sowohl bei der Quellentrennung als auch bei der Schätzung der Anzahl an Quellen ist ein Kernbeitrag dieser Arbeit das Konzept der âModulationâ, welches wichtig ist, um die Strategien von Menschen mittels Computern nachzuahmen. Unsere vorgeschlagene Common Fate Transformation ist eine adäquate Darstellung, um die Ăberlappung von Signalen fĂźr die Trennung zugänglich zu machen und eine Inspektion unseres DNN-Zählmodells ergab schlieĂlich, dass sich auch hier modulationsähnliche Merkmale ďŹnden lassen
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Adaptive Noise Reduction for Sound Event Detection Using Subband-Weighted NMF
Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). First, a scheme for noise dictionary learning from the input noisy signal is employed by the technique of robust NMF, which supports adaptation to noise variations. The estimated noise dictionary is used to develop a supervised source separation framework in combination with a pre-trained event dictionary. Second, to improve the separation quality, we extend the basic NMF model to a weighted form, with the aim of varying the relative importance of the different components when separating a target sound event from noise. With properly designed weights, the separation process is forced to rely more on those dominant event components, whereas the noise gets greatly suppressed. The proposed method is evaluated on a dataset of the rare sound event detection task of the DCASE 2017 challenge, and achieves comparable results to the top-ranking system based on convolutional recurrent neural networks (CRNNs). The proposed weighted NMF method shows an excellent noise reduction ability, and achieves an improvement of an F-score by 5%, compared to the unweighted approach
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Sound events often occur in unstructured environments where they exhibit wide
variations in their frequency content and temporal structure. Convolutional
neural networks (CNN) are able to extract higher level features that are
invariant to local spectral and temporal variations. Recurrent neural networks
(RNNs) are powerful in learning the longer term temporal context in the audio
signals. CNNs and RNNs as classifiers have recently shown improved performances
over established methods in various sound recognition tasks. We combine these
two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it
on a polyphonic sound event detection task. We compare the performance of the
proposed CRNN method with CNN, RNN, and other established methods, and observe
a considerable improvement for four different datasets consisting of everyday
sound events.Comment: Accepted for IEEE Transactions on Audio, Speech and Language
Processing, Special Issue on Sound Scene and Event Analysi
A Four-Stage Data Augmentation Approach to ResNet-Conformer Based Acoustic Modeling for Sound Event Localization and Detection
In this paper, we propose a novel four-stage data augmentation approach to
ResNet-Conformer based acoustic modeling for sound event localization and
detection (SELD). First, we explore two spatial augmentation techniques, namely
audio channel swapping (ACS) and multi-channel simulation (MCS), to deal with
data sparsity in SELD. ACS and MDS focus on augmenting the limited training
data with expanding direction of arrival (DOA) representations such that the
acoustic models trained with the augmented data are robust to localization
variations of acoustic sources. Next, time-domain mixing (TDM) and
time-frequency masking (TFM) are also investigated to deal with overlapping
sound events and data diversity. Finally, ACS, MCS, TDM and TFM are combined in
a step-by-step manner to form an effective four-stage data augmentation scheme.
Tested on the Detection and Classification of Acoustic Scenes and Events
(DCASE) 2020 data sets, our proposed augmentation approach greatly improves the
system performance, ranking our submitted system in the first place in the SELD
task of DCASE 2020 Challenge. Furthermore, we employ a ResNet-Conformer
architecture to model both global and local context dependencies of an audio
sequence to yield further gains over those architectures used in the DCASE 2020
SELD evaluations.Comment: 12 pages, 8 figure
Acoustic event detection and localization using distributed microphone arrays
Automatic acoustic scene analysis is a complex task that involves several functionalities: detection (time), localization (space), separation, recognition, etc. This thesis focuses on both acoustic event detection (AED) and acoustic source localization (ASL), when several sources may be simultaneously present in a room. In particular, the experimentation work is carried out with a meeting-room scenario. Unlike previous works that either employed models of all possible sound combinations or additionally used video signals, in this thesis, the time overlapping sound problem is tackled by exploiting the signal diversity that results from the usage of multiple microphone array beamformers.
The core of this thesis work is a rather computationally efficient approach that consists of three processing stages. In the first, a set of (null) steering beamformers is used to carry out diverse partial signal separations, by using multiple arbitrarily located linear microphone arrays, each of them composed of a small number of microphones. In the second stage, each of the beamformer output goes through a classification step, which uses models for all the targeted sound classes (HMM-GMM, in the experiments). Then, in a third stage, the classifier scores, either being intra- or inter-array, are combined using a probabilistic criterion (like MAP) or a machine learning fusion technique (fuzzy integral (FI), in the experiments).
The above-mentioned processing scheme is applied in this thesis to a set of complexity-increasing problems, which are defined by the assumptions made regarding identities (plus time endpoints) and/or positions of sounds. In fact, the thesis report starts with the problem of unambiguously mapping the identities to the positions, continues with AED (positions assumed) and ASL (identities assumed), and ends with the integration of AED and ASL in a single system, which does not need any assumption about identities or positions.
The evaluation experiments are carried out in a meeting-room scenario, where two sources are temporally overlapped; one of them is always speech and the other is an acoustic event from a pre-defined set. Two different databases are used, one that is produced by merging signals actually recorded in the UPCÂżs department smart-room, and the other consists of overlapping sound signals directly recorded in the same room and in a rather spontaneous way. From the experimental results with a single array, it can be observed that the proposed detection system performs better than either the model based system or a blind source separation based system. Moreover, the product rule based combination and the FI based fusion of the scores resulting from the multiple arrays improve the accuracies further. On the other hand, the posterior position assignment is performed with a very small error rate.
Regarding ASL and assuming an accurate AED system output, the 1-source localization performance of the proposed system is slightly better than that of the widely-used SRP-PHAT system, working in an event-based mode, and it even performs significantly better than the latter one in the more complex 2-source scenario. Finally, though the joint system suffers from a slight degradation in terms of classification accuracy with respect to the case where the source positions are known, it shows the advantage of carrying out the two tasks, recognition and localization, with a single system, and it allows the inclusion of information about the prior probabilities of the source positions. It is worth noticing also that, although the acoustic scenario used for experimentation is rather limited, the approach and its formalism were developed for a general case, where the number and identities of sources are not constrained
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