7,759 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
Reliability-Informed Beat Tracking of Musical Signals
Abstract—A new probabilistic framework for beat tracking of musical audio is presented. The method estimates the time between consecutive beat events and exploits both beat and non-beat information by explicitly modeling non-beat states. In addition to the beat times, a measure of the expected accuracy of the estimated beats is provided. The quality of the observations used for beat tracking is measured and the reliability of the beats is automatically calculated. A k-nearest neighbor regression algorithm is proposed to predict the accuracy of the beat estimates. The performance of the beat tracking system is statistically evaluated using a database of 222 musical signals of various genres. We show that modeling non-beat states leads to a significant increase in performance. In addition, a large experiment where the parameters of the model are automatically learned has been completed. Results show that simple approximations for the parameters of the model can be used. Furthermore, the performance of the system is compared with existing algorithms. Finally, a new perspective for beat tracking evaluation is presented. We show how reliability information can be successfully used to increase the mean performance of the proposed algorithm and discuss how far automatic beat tracking is from human tapping. Index Terms—Beat-tracking, beat quality, beat-tracking reliability, k-nearest neighbor (k-NN) regression, music signal processing. I
A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor
In this paper, a siamese DNN model is proposed to learn the characteristics
of the audio dynamic range compressor (DRC). This facilitates an intelligent
control system that uses audio examples to configure the DRC, a widely used
non-linear audio signal conditioning technique in the areas of music
production, speech communication and broadcasting. Several alternative siamese
DNN architectures are proposed to learn feature embeddings that can
characterise subtle effects due to dynamic range compression. These models are
compared with each other as well as handcrafted features proposed in previous
work. The evaluation of the relations between the hyperparameters of DNN and
DRC parameters are also provided. The best model is able to produce a universal
feature embedding that is capable of predicting multiple DRC parameters
simultaneously, which is a significant improvement from our previous research.
The feature embedding shows better performance than handcrafted audio features
when predicting DRC parameters for both mono-instrument audio loops and
polyphonic music pieces.Comment: 8 pages, accepted in IJCNN 201
Sensing and mapping for interactive performance
This paper describes a trans-domain mapping (TDM) framework for translating meaningful activities from one creative domain onto another. The multi-disciplinary framework is designed to facilitate an intuitive and non-intrusive interactive multimedia performance interface that offers the users or performers real-time control of multimedia events using their physical movements. It is intended to be a highly dynamic real-time performance tool, sensing and tracking activities and changes, in order to provide interactive multimedia performances.
From a straightforward definition of the TDM framework, this paper reports several implementations and multi-disciplinary collaborative projects using the proposed framework, including a motion and colour-sensitive system, a sensor-based system for triggering musical events, and a distributed multimedia server for audio mapping of a real-time face tracker, and discusses different aspects of mapping strategies in their context.
Plausible future directions, developments and exploration with the proposed framework, including stage augmenta tion, virtual and augmented reality, which involve sensing and mapping of physical and non-physical changes onto multimedia control events, are discussed
Recognition of Harmonic Sounds in Polyphonic Audio using a Missing Feature Approach: Extended Report
A method based on local spectral features and missing feature techniques
is proposed for the recognition of harmonic sounds in mixture
signals. A mask estimation algorithm is proposed for identifying
spectral regions that contain reliable information for each sound
source and then bounded marginalization is employed to treat the
feature vector elements that are determined as unreliable. The proposed
method is tested on musical instrument sounds due to the
extensive availability of data but it can be applied on other sounds
(i.e. animal sounds, environmental sounds), whenever these are harmonic.
In simulations the proposed method clearly outperformed a
baseline method for mixture signals
Towards the automated analysis of simple polyphonic music : a knowledge-based approach
PhDMusic understanding is a process closely related to the knowledge and experience
of the listener. The amount of knowledge required is relative to the
complexity of the task in hand.
This dissertation is concerned with the problem of automatically decomposing
musical signals into a score-like representation. It proposes that, as
with humans, an automatic system requires knowledge about the signal and
its expected behaviour to correctly analyse music.
The proposed system uses the blackboard architecture to combine the
use of knowledge with data provided by the bottom-up processing of the
signal's information. Methods are proposed for the estimation of pitches,
onset times and durations of notes in simple polyphonic music.
A method for onset detection is presented. It provides an alternative to
conventional energy-based algorithms by using phase information. Statistical
analysis is used to create a detection function that evaluates the expected
behaviour of the signal regarding onsets.
Two methods for multi-pitch estimation are introduced. The first concentrates
on the grouping of harmonic information in the frequency-domain.
Its performance and limitations emphasise the case for the use of high-level
knowledge.
This knowledge, in the form of the individual waveforms of a single
instrument, is used in the second proposed approach. The method is based
on a time-domain linear additive model and it presents an alternative to
common frequency-domain approaches.
Results are presented and discussed for all methods, showing that, if
reliably generated, the use of knowledge can significantly improve the quality
of the analysis.Joint Information Systems Committee (JISC) in the UK National Science Foundation (N.S.F.) in the United states. Fundacion Gran Mariscal Ayacucho in Venezuela
Towards an interactive framework for robot dancing applications
Estágio realizado no INESC-Porto e orientado pelo Prof. Doutor Fabien GouyonTese de mestrado integrado. Engenharia Electrotécnica e de Computadores - Major Telecomunicações. Faculdade de Engenharia. Universidade do Porto. 200
- …