43 research outputs found
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network
Music creation is typically composed of two parts: composing the musical
score, and then performing the score with instruments to make sounds. While
recent work has made much progress in automatic music generation in the
symbolic domain, few attempts have been made to build an AI model that can
render realistic music audio from musical scores. Directly synthesizing audio
with sound sample libraries often leads to mechanical and deadpan results,
since musical scores do not contain performance-level information, such as
subtle changes in timing and dynamics. Moreover, while the task may sound like
a text-to-speech synthesis problem, there are fundamental differences since
music audio has rich polyphonic sounds. To build such an AI performer, we
propose in this paper a deep convolutional model that learns in an end-to-end
manner the score-to-audio mapping between a symbolic representation of music
called the piano rolls and an audio representation of music called the
spectrograms. The model consists of two subnets: the ContourNet, which uses a
U-Net structure to learn the correspondence between piano rolls and
spectrograms and to give an initial result; and the TextureNet, which further
uses a multi-band residual network to refine the result by adding the spectral
texture of overtones and timbre. We train the model to generate music clips of
the violin, cello, and flute, with a dataset of moderate size. We also present
the result of a user study that shows our model achieves higher mean opinion
score (MOS) in naturalness and emotional expressivity than a WaveNet-based
model and two commercial sound libraries. We open our source code at
https://github.com/bwang514/PerformanceNetComment: 8 pages, 6 figures, AAAI 2019 camera-ready versio
Sparks of Large Audio Models: A Survey and Outlook
This survey paper provides a comprehensive overview of the recent
advancements and challenges in applying large language models to the field of
audio signal processing. Audio processing, with its diverse signal
representations and a wide range of sources--from human voices to musical
instruments and environmental sounds--poses challenges distinct from those
found in traditional Natural Language Processing scenarios. Nevertheless,
\textit{Large Audio Models}, epitomized by transformer-based architectures,
have shown marked efficacy in this sphere. By leveraging massive amount of
data, these models have demonstrated prowess in a variety of audio tasks,
spanning from Automatic Speech Recognition and Text-To-Speech to Music
Generation, among others. Notably, recently these Foundational Audio Models,
like SeamlessM4T, have started showing abilities to act as universal
translators, supporting multiple speech tasks for up to 100 languages without
any reliance on separate task-specific systems. This paper presents an in-depth
analysis of state-of-the-art methodologies regarding \textit{Foundational Large
Audio Models}, their performance benchmarks, and their applicability to
real-world scenarios. We also highlight current limitations and provide
insights into potential future research directions in the realm of
\textit{Large Audio Models} with the intent to spark further discussion,
thereby fostering innovation in the next generation of audio-processing
systems. Furthermore, to cope with the rapid development in this area, we will
consistently update the relevant repository with relevant recent articles and
their open-source implementations at
https://github.com/EmulationAI/awesome-large-audio-models.Comment: work in progress, Repo URL:
https://github.com/EmulationAI/awesome-large-audio-model
AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining
Although audio generation shares commonalities across different types of
audio, such as speech, music, and sound effects, designing models for each type
requires careful consideration of specific objectives and biases that can
significantly differ from those of other types. To bring us closer to a unified
perspective of audio generation, this paper proposes a framework that utilizes
the same learning method for speech, music, and sound effect generation. Our
framework introduces a general representation of audio, called "language of
audio" (LOA). Any audio can be translated into LOA based on AudioMAE, a
self-supervised pre-trained representation learning model. In the generation
process, we translate any modalities into LOA by using a GPT-2 model, and we
perform self-supervised audio generation learning with a latent diffusion model
conditioned on LOA. The proposed framework naturally brings advantages such as
in-context learning abilities and reusable self-supervised pretrained AudioMAE
and latent diffusion models. Experiments on the major benchmarks of
text-to-audio, text-to-music, and text-to-speech demonstrate state-of-the-art
or competitive performance against previous approaches. Our code, pretrained
model, and demo are available at https://audioldm.github.io/audioldm2.Comment: AudioLDM 2 project page is https://audioldm.github.io/audioldm
An Industry Driven Genre Classification Application using Natural Language Processing
With the advent of digitized music, many online streaming companies such as Spotify have capitalized on a listener’s need for a common stream platform. An essential component of such a platform is the recommender systems that suggest to the constituent user base, related tracks, albums and artists. In order to sustain such a recommender system, labeling data to indicate which genre it belongs to is essential. Most recent academic publications that deal with music genre classification focus on the use of deep neural networks developed and applied within the music genre classification domain. This thesis attempts to use some of the highly sophisticated techniques, such as Hierarchical Attention Networks that exist within the text classification domain in order to classify tracks of different genres. In order to do this, the music is first separated into different tracks (drums, vocals, bass and accompaniment) and converted into symbolic text data. Due to the sophistication of the distributed machine learning system (over five computers, each possessing a graphical processing units greater than a GTX 1070) present in this thesis, it is capable of classifying contemporary genres with an impressive peak accuracy of over 93%, when comparing the results with that of competing classifiers. It is also argued that through the use text classification, the ex- pert domain knowledge which musicians and people involved with musicological techniques, can be attracted to improving reccomender systems within the music information retrieval research domain
Deep Learning Methods for Instrument Separation and Recognition
This thesis explores deep learning methods for timbral information processing in polyphonic music analysis. It encompasses two primary tasks: Music Source Separation (MSS) and Instrument Recognition, with focus on applying domain knowledge and utilising dense arrangements of skip-connections in the frameworks in order to reduce the number of trainable parameters and create more efficient models. Musically-motivated Convolutional Neural Network (CNN) architectures are introduced, emphasizing kernels with vertical, square, and horizontal shapes. This design choice allows for the extraction of essential harmonic and percussive features, which enhances the discrimination of different instruments. Notably, this methodology proves valuable for Harmonic-Percussive Source Separation (HPSS) and instrument recognition tasks. A significant challenge in MSS is generalising to new instrument types and music styles. To address this, a versatile framework for adversarial unsupervised domain adaptation for source separation is proposed, particularly beneficial when labeled data for specific instruments is unavailable. The curation of the Tap & Fiddle dataset is another contribution of the research, offering mixed and isolated stem recordings of traditional Scandinavian fiddle tunes, along with foot-tapping accompaniments, fostering research in source separation and metrical expression analysis within these musical styles. Since our perception of timbre is affected in different ways by transient and stationary parts of sound, the research investigates the potential of Transient Stationary-Noise Decomposition (TSND) as a preprocessing step for frame-level recognition. A method that performs TSND of spectrograms and feeds the decomposed spectrograms to a neural classifier is proposed. Furthermore, this thesis introduces a novel deep learning-based approach for pitch streaming, treating the task as a note-level instrument classification. Such an approach is modular, meaning that it can also successfully stream predicted note-events and not only labelled ground truth note-event information to corresponding instruments. Therefore, the proposed pitch streaming method enables third-party multi-pitch estimation algorithms to perform multi-instrument AMT
Sound Event Detection by Exploring Audio Sequence Modelling
Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing a sound recognition system are, which portion of a sound event should the system analyse, and what proportion of a sound event should the system process in order to claim a confident detection of that particular sound event. While the classification of sound events has improved a lot in recent years, it is considered that the temporal-segmentation of sound events has not improved in the same extent. The aim of this thesis is to propose and develop methods to improve the segmentation and classification of everyday sound events in SED models. In particular, this thesis explores the segmentation of sound events by investigating audio sequence encoding-based and audio sequence modelling-based methods, in an effort to improve the overall sound event detection performance. In the first phase of this thesis, efforts are put towards improving sound event detection by explicitly conditioning the audio sequence representations of an SED model using sound activity detection (SAD) and onset detection. To achieve this, we propose multi-task learning-based SED models in which SAD and onset detection are used as auxiliary tasks for the SED task. The next part of this thesis explores self-attention-based audio sequence modelling, which aggregates audio representations based on temporal relations within and between sound events, scored on the basis of the similarity of sound event portions in audio event sequences. We propose SED models that include memory-controlled, adaptive, dynamic, and source separation-induced self-attention variants, with the aim to improve overall sound recognition
Affective social anthropomorphic intelligent system
Human conversational styles are measured by the sense of humor, personality,
and tone of voice. These characteristics have become essential for
conversational intelligent virtual assistants. However, most of the
state-of-the-art intelligent virtual assistants (IVAs) are failed to interpret
the affective semantics of human voices. This research proposes an
anthropomorphic intelligent system that can hold a proper human-like
conversation with emotion and personality. A voice style transfer method is
also proposed to map the attributes of a specific emotion. Initially, the
frequency domain data (Mel-Spectrogram) is created by converting the temporal
audio wave data, which comprises discrete patterns for audio features such as
notes, pitch, rhythm, and melody. A collateral CNN-Transformer-Encoder is used
to predict seven different affective states from voice. The voice is also fed
parallelly to the deep-speech, an RNN model that generates the text
transcription from the spectrogram. Then the transcripted text is transferred
to the multi-domain conversation agent using blended skill talk,
transformer-based retrieve-and-generate generation strategy, and beam-search
decoding, and an appropriate textual response is generated. The system learns
an invertible mapping of data to a latent space that can be manipulated and
generates a Mel-spectrogram frame based on previous Mel-spectrogram frames to
voice synthesize and style transfer. Finally, the waveform is generated using
WaveGlow from the spectrogram. The outcomes of the studies we conducted on
individual models were auspicious. Furthermore, users who interacted with the
system provided positive feedback, demonstrating the system's effectiveness.Comment: Multimedia Tools and Applications (2023
Representation Analysis Methods to Model Context for Speech Technology
Speech technology has developed to levels equivalent with human parity through the use of deep neural networks. However, it is unclear how the learned dependencies within these networks can be attributed to metrics such as recognition performance. This research focuses on strategies to interpret and exploit these learned context dependencies to improve speech recognition models. Context dependency analysis had not yet been explored for speech recognition networks.
In order to highlight and observe dependent representations within speech recognition models, a novel analysis framework is proposed. This analysis framework uses statistical correlation indexes to compute the coefficiency between neural representations. By comparing the coefficiency of neural representations between models using different approaches, it is possible to observe specific context dependencies within network layers. By providing insights on context dependencies it is then possible to adapt modelling approaches to become more computationally efficient and improve recognition performance. Here the performance of End-to-End speech recognition models are analysed, providing insights on the acoustic and language modelling context dependencies. The modelling approach for a speaker recognition task is adapted to exploit acoustic context dependencies and reach comparable performance with the state-of-the-art methods, reaching 2.89% equal error rate using the Voxceleb1 training and test sets with 50% of the parameters. Furthermore, empirical analysis of the
role of acoustic context for speech emotion recognition modelling revealed that emotion cues are presented as a distributed event. These analyses and results for speech recognition applications aim to provide objective direction for future development of automatic speech recognition systems
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Musical source separation with deep learning and large-scale datasets
Throughout this thesis we will explore automatic music source separation by utilizing modern (at the time of writing) techniques and tools from machine learning and big data processing. The bulk of this work was carried out between 2016 and 2019.
In Chapter 2 we conduct a review of source separation literature. We start by outlining a subset of applications of source separation in some depth. We describe some of the early, pioneering work in automatic source separation: Auditory Scene Analysis, and its digital counterpart, Computational Auditory Scene Analysis.
We then introduce matrix decomposition-based methods such as Independent Component Analysis and Non-Negative Matrix factorization, and pitch informed methods where the separation algorithm is guided by pitch information that is known a priori. We brie y discuss user-guided methods, before conducting a thorough review of Deep Learning based source separation, including recurrent, convolutional, deep clustering-based, and Generative Adversarial Networks.
We then proceed to describe common evaluation metrics
and training datasets. Finally, we list a number of current challenges and drawbacks of current systems.
Chapter 3 focuses on datasets for musical source separation. First we show the growth of dataset sizes for both machine learning in general and music information retrieval specifically. We give several examples of the complexities and idiosyncrasies that are intrinsic to music datasets. We then proceed to present a method for extracting ground truth data for source separation from large unstructured musical catalogs.
In Chapter 4 we design a novel deep learning-based source separation algorithm. Motivation is provided by means of a musicological study1 that showed the high importance of vocals relative to other musical factors, in the minds of listeners. At the core of the vocal separation algorithm is the U-Net, a deep learning architecture that uses skip connections to preserve fine-grained detail. It was originally developed in the biomedical imaging domain, and later adapted to image-to-image translation. We adapt it to the source separation domain by treating spectrograms as images, and we use the dataset mining methods from Chapter 3 to generate sufficiently large training data. We evaluate our model objectively using standard evaluation metrics, subjectively using \crowdsourced" human subjects. To the best of our knowledge, this is the first use of U-Nets for source separation.
In the introduction above we proposed joint learning to optimize source separation and other objectives. In Chapter 5 we investigate one such instance: multi-task learning of vocal removal and vocal pitch tracking. We combine the vocal separation model from Chapter 4 with a state of the art pitch salience estimation model2, exploring several ways of combining the two models. We find that vocal pitch estimation benefits from joint learning when the two tasks are trained in sequence, with the source separation model preceding the pitch estimation model. We also report benefits from fine-tuning by iteratively applying the model.
Chapter 6 extends the U-Net model to multiple instruments. In order to minimize the phase artifacts that were a common issue in Chapter 4, we modify the model to operate in the complex domain. We run experiments with several loss functions: Time-domain loss, magnitude-only frequency domain loss, and joint time and frequency-domain loss. Our experiments are evaluated both objectively and subjectively, and we carry out extensive qualitative analysis to investigate the effects of complex masking.
Finally, we conclude the thesis in Chapter 7 by summarizing this work and highlighting several future directions of research