87 research outputs found
Comparison for Improvements of Singing Voice Detection System Based on Vocal Separation
Singing voice detection is the task to identify the frames which contain the
singer vocal or not. It has been one of the main components in music
information retrieval (MIR), which can be applicable to melody extraction,
artist recognition, and music discovery in popular music. Although there are
several methods which have been proposed, a more robust and more complete
system is desired to improve the detection performance. In this paper, our
motivation is to provide an extensive comparison in different stages of singing
voice detection. Based on the analysis a novel method was proposed to build a
more efficiently singing voice detection system. In the proposed system, there
are main three parts. The first is a pre-process of singing voice separation to
extract the vocal without the music. The improvements of several singing voice
separation methods were compared to decide the best one which is integrated to
singing voice detection system. And the second is a deep neural network based
classifier to identify the given frames. Different deep models for
classification were also compared. The last one is a post-process to filter out
the anomaly frame on the prediction result of the classifier. The median filter
and Hidden Markov Model (HMM) based filter as the post process were compared.
Through the step by step module extension, the different methods were compared
and analyzed. Finally, classification performance on two public datasets
indicates that the proposed approach which based on the Long-term Recurrent
Convolutional Networks (LRCN) model is a promising alternative.Comment: 15 page
Trennung und SchĂ€tzung der Anzahl von Audiosignalquellen mit Zeit- und FrequenzĂŒberlappung
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 ïŹrst 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, conïŹrming 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 ïŹnd 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. FĂŒr diese Mischungen ist die automatische Quellentrennung oder die Bestimmung der Anzahl an Quellen eine anspruchsvolle Aufgabe. Eine hĂ€uïŹge Annahme bei der Verarbeitung von gemischten Signalen im Zeit-Frequenzbereich ist, dass die Quellen sich nicht vollstĂ€ndig ĂŒberlappen. In dieser Arbeit betrachten wir jedoch einige FĂ€lle, 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-ReprĂ€sentationen und leistungsfĂ€higere Modelle notwendig sind.
Um die zwei genannten Probleme zu bewĂ€ltigen, verwenden wir sowohl konventionelle Signalverbeitungsmethoden als auch tiefgehende neuronale Netze (DNN). Wir gehen zunĂ€chst auf das Problem der Quellentrennung fĂŒr 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 SchĂ€tzung der Grundfrequenz als zusĂ€tzliche Information nutzt. FĂŒr FĂ€lle, in denen diese SchĂ€tzungen nicht verfĂŒgbar sind, stellen wir ein unĂŒberwachtes Modell vor, das inspiriert ist von der Art und Weise, wie Menschen zeitverĂ€nderliche Quellen gruppieren (Common Fate). Dieser Beitrag enthĂ€lt eine neuartige ReprĂ€sentation, die die Separierbarkeit fĂŒr ĂŒberlappte 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 beschĂ€ftigen wir uns mit der SchĂ€tzung der Anzahl von Quellen in einer Mischung, was fĂŒr reale Szenarien wichtig ist. Unsere Arbeit an der SchĂ€tzung der Anzahl war motiviert durch eine Studie, die zeigt, wie wir Menschen diese Aufgabe angehen. Dies hat uns dazu veranlasst, eigene Hörexperimente durchzufĂŒhren, die bestĂ€tigten, dass Menschen nur in der Lage sind, die Anzahl von bis zu vier Quellen korrekt abzuschĂ€tzen. Um nun die Frage zu beantworten, ob Maschinen dies Ă€hnlich 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 SchĂ€tzung 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 adĂ€quate Darstellung, um die Ăberlappung von Signalen fĂŒr die Trennung zugĂ€nglich zu machen und eine Inspektion unseres DNN-ZĂ€hlmodells ergab schlieĂlich, dass sich auch hier modulationsĂ€hnliche Merkmale ïŹnden lassen
A Review of Deep Learning Techniques for Speech Processing
The field of speech processing has undergone a transformative shift with the
advent of deep learning. The use of multiple processing layers has enabled the
creation of models capable of extracting intricate features from speech data.
This development has paved the way for unparalleled advancements in speech
recognition, text-to-speech synthesis, automatic speech recognition, and
emotion recognition, propelling the performance of these tasks to unprecedented
heights. The power of deep learning techniques has opened up new avenues for
research and innovation in the field of speech processing, with far-reaching
implications for a range of industries and applications. This review paper
provides a comprehensive overview of the key deep learning models and their
applications in speech-processing tasks. We begin by tracing the evolution of
speech processing research, from early approaches, such as MFCC and HMM, to
more recent advances in deep learning architectures, such as CNNs, RNNs,
transformers, conformers, and diffusion models. We categorize the approaches
and compare their strengths and weaknesses for solving speech-processing tasks.
Furthermore, we extensively cover various speech-processing tasks, datasets,
and benchmarks used in the literature and describe how different deep-learning
networks have been utilized to tackle these tasks. Additionally, we discuss the
challenges and future directions of deep learning in speech processing,
including the need for more parameter-efficient, interpretable models and the
potential of deep learning for multimodal speech processing. By examining the
field's evolution, comparing and contrasting different approaches, and
highlighting future directions and challenges, we hope to inspire further
research in this exciting and rapidly advancing field
16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)
The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc
Analysing multi-person timing in music and movement : event based methods
Accurate timing of movement in the hundreds of milliseconds range is a hallmark of human activities such as music and dance. Its study requires accurate measurement of the times of events (often called responses) based on the movement or acoustic record. This chapter provides a comprehensive over - view of methods developed to capture, process, analyse, and model individual and group timing [...] This chapter is structured in five main sections, as follows. We start with a review of data capture methods, working, in turn, through a low cost system to research simple tapping, complex movements, use of video, inertial measurement units, and dedicated sensorimotor synchronisation software. This is followed by a section on music performance, which includes topics on the selection of music materials, sound recording, and system latency. The identification of events in the data stream can be challenging and this topic is treated in the next section, first for movement then for music. Finally, we cover methods of analysis, including alignment of the channels, computation of between channel asynchrony errors and modelling of the data set
Advances in deep learning methods for speech recognition and understanding
Ce travail expose plusieurs Ă©tudes dans les domaines de
la reconnaissance de la parole et
compréhension du langage parlé.
La compréhension sémantique du langage parlé est un sous-domaine important
de l'intelligence artificielle.
Le traitement de la parole intéresse depuis longtemps les chercheurs,
puisque la parole est une des charactĂ©ristiques qui definit l'ĂȘtre humain.
Avec le développement du réseau neuronal artificiel,
le domaine a connu une Ă©volution rapide
à la fois en terme de précision et de perception humaine.
Une autre étape importante a été franchie avec le développement
d'approches bout en bout.
De telles approches permettent une coadaptation de toutes
les parties du modĂšle, ce qui augmente ainsi les performances,
et ce qui simplifie la procédure d'entrainement.
Les modÚles de bout en bout sont devenus réalisables avec la quantité croissante
de données disponibles, de ressources informatiques et,
surtout, avec de nombreux développements architecturaux innovateurs.
NĂ©anmoins, les approches traditionnelles (qui ne sont pas bout en bout)
sont toujours pertinentes pour le traitement de la parole en raison
des données difficiles dans les environnements bruyants,
de la parole avec un accent et de la grande variété de dialectes.
Dans le premier travail, nous explorons la reconnaissance de la parole hybride
dans des environnements bruyants.
Nous proposons de traiter la reconnaissance de la parole,
qui fonctionne dans
un nouvel environnement composé de différents bruits inconnus,
comme une tĂąche d'adaptation de domaine.
Pour cela, nous utilisons la nouvelle technique Ă l'Ă©poque
de l'adaptation du domaine antagoniste.
En résumé, ces travaux antérieurs proposaient de former
des caractéristiques de maniÚre à ce qu'elles soient distinctives
pour la tĂąche principale, mais non-distinctive pour la tĂąche secondaire.
Cette tĂąche secondaire est conçue pour ĂȘtre la tĂąche de reconnaissance de domaine.
Ainsi, les fonctionnalités entraßnées sont invariantes vis-à -vis du domaine considéré.
Dans notre travail, nous adoptons cette technique et la modifions pour
la tĂąche de reconnaissance de la parole dans un environnement bruyant.
Dans le second travail, nous développons une méthode générale
pour la régularisation des réseaux génératif récurrents.
Il est connu que les réseaux récurrents ont souvent des difficultés à rester
sur le mĂȘme chemin, lors de la production de sorties longues.
Bien qu'il soit possible d'utiliser des réseaux bidirectionnels pour
une meilleure traitement de séquences pour l'apprentissage des charactéristiques,
qui n'est pas applicable au cas génératif.
Nous avons développé un moyen d'améliorer la cohérence de
la production de longues séquences avec des réseaux récurrents.
Nous proposons un moyen de construire un modÚle similaire à un réseau bidirectionnel.
L'idée centrale est d'utiliser une perte L2 entre
les réseaux récurrents génératifs vers l'avant et vers l'arriÚre.
Nous fournissons une évaluation expérimentale sur
une multitude de tùches et d'ensembles de données,
y compris la reconnaissance vocale,
le sous-titrage d'images et la modélisation du langage.
Dans le troisiÚme article, nous étudions la possibilité de développer
un identificateur d'intention de bout en bout pour la compréhension du langage parlé.
La compréhension sémantique du langage parlé est une étape importante vers
le développement d'une intelligence artificielle de type humain.
Nous avons vu que les approches de bout en bout montrent
des performances élevées sur les tùches, y compris la traduction automatique et
la reconnaissance de la parole.
Nous nous inspirons des travaux antérieurs pour développer
un systĂšme de bout en bout pour la reconnaissance de l'intention.This work presents several studies in the areas of speech recognition and
understanding.
The semantic speech understanding is an important sub-domain of the
broader field of artificial intelligence.
Speech processing has had interest from the researchers for long time
because language is one of the defining characteristics of a human being.
With the development of neural networks, the domain has seen rapid progress
both in terms of accuracy and human perception.
Another important milestone was achieved with the development of
end-to-end approaches.
Such approaches allow co-adaptation of all the parts of the model
thus increasing the performance, as well as simplifying the training
procedure.
End-to-end models became feasible with the increasing amount of available
data, computational resources, and most importantly with many novel
architectural developments.
Nevertheless, traditional, non end-to-end, approaches are still relevant
for speech processing due to challenging data in noisy environments,
accented speech, and high variety of dialects.
In the first work, we explore the hybrid speech recognition in noisy
environments.
We propose to treat the recognition in the unseen noise condition
as the domain adaptation task.
For this, we use the novel at the time technique of the adversarial
domain adaptation.
In the nutshell, this prior work proposed to train features in such
a way that they are discriminative for the primary task,
but non-discriminative for the secondary task.
This secondary task is constructed to be the domain recognition task.
Thus, the features trained are invariant towards the domain at hand.
In our work, we adopt this technique and modify it for the task of
noisy speech recognition.
In the second work, we develop a general method for regularizing
the generative recurrent networks.
It is known that the recurrent networks frequently have difficulties
staying on same track when generating long outputs.
While it is possible to use bi-directional networks for better
sequence aggregation for feature learning, it is not applicable
for the generative case.
We developed a way improve the consistency of generating long sequences
with recurrent networks.
We propose a way to construct a model similar to bi-directional network.
The key insight is to use a soft L2 loss between the forward and
the backward generative recurrent networks.
We provide experimental evaluation on a multitude of tasks and datasets,
including speech recognition, image captioning, and language modeling.
In the third paper, we investigate the possibility of developing
an end-to-end intent recognizer for spoken language understanding.
The semantic spoken language understanding is an important
step towards developing a human-like artificial intelligence.
We have seen that the end-to-end approaches show high
performance on the tasks including machine translation and speech recognition.
We draw the inspiration from the prior works to develop
an end-to-end system for intent recognition
FPGA implementation of a LSTM Neural Network
Este trabalho pretende fazer uma implementação customizada, em Hardware, duma Rede Neuronal Long Short-Term Memory. O modelo python, assim como a descrição Verilog, e sĂntese RTL, encontram-se terminadas. Falta apenas fazer o benchmarking e a integração de um sistema de aprendizagem
- âŠ