1,472 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
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
A Fully Convolutional Deep Auditory Model for Musical Chord Recognition
Chord recognition systems depend on robust feature extraction pipelines.
While these pipelines are traditionally hand-crafted, recent advances in
end-to-end machine learning have begun to inspire researchers to explore
data-driven methods for such tasks. In this paper, we present a chord
recognition system that uses a fully convolutional deep auditory model for
feature extraction. The extracted features are processed by a Conditional
Random Field that decodes the final chord sequence. Both processing stages are
trained automatically and do not require expert knowledge for optimising
parameters. We show that the learned auditory system extracts musically
interpretable features, and that the proposed chord recognition system achieves
results on par or better than state-of-the-art algorithms.Comment: In Proceedings of the 2016 IEEE 26th International Workshop on
Machine Learning for Signal Processing (MLSP), Vietro sul Mare, Ital
The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation
With recent breakthroughs in artificial neural networks, deep generative
models have become one of the leading techniques for computational creativity.
Despite very promising progress on image and short sequence generation,
symbolic music generation remains a challenging problem since the structure of
compositions are usually complicated. In this study, we attempt to solve the
melody generation problem constrained by the given chord progression. This
music meta-creation problem can also be incorporated into a plan recognition
system with user inputs and predictive structural outputs. In particular, we
explore the effect of explicit architectural encoding of musical structure via
comparing two sequential generative models: LSTM (a type of RNN) and WaveNet
(dilated temporal-CNN). As far as we know, this is the first study of applying
WaveNet to symbolic music generation, as well as the first systematic
comparison between temporal-CNN and RNN for music generation. We conduct a
survey for evaluation in our generations and implemented Variable Markov Oracle
in music pattern discovery. Experimental results show that to encode structure
more explicitly using a stack of dilated convolution layers improved the
performance significantly, and a global encoding of underlying chord
progression into the generation procedure gains even more.Comment: 8 pages, 13 figure
VGM-RNN: Recurrent Neural Networks for Video Game Music Generation
The recent explosion of interest in deep neural networks has affected and in some cases reinvigorated work in fields as diverse as natural language processing, image recognition, speech recognition and many more. For sequence learning tasks, recurrent neural networks and in particular LSTM-based networks have shown promising results. Recently there has been interest – for example in the research by Google’s Magenta team – in applying so-called “language modeling” recurrent neural networks to musical tasks, including for the automatic generation of original music. In this work we demonstrate our own LSTM-based music language modeling recurrent network. We show that it is able to learn musical features from a MIDI dataset and generate output that is musically interesting while demonstrating features of melody, harmony and rhythm. We source our dataset from VGMusic.com, a collection of user-submitted MIDI transcriptions of video game songs, and attempt to generate output which emulates this kind of music
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