3,576 research outputs found
Multimodal music information processing and retrieval: survey and future challenges
Towards improving the performance in various music information processing
tasks, recent studies exploit different modalities able to capture diverse
aspects of music. Such modalities include audio recordings, symbolic music
scores, mid-level representations, motion, and gestural data, video recordings,
editorial or cultural tags, lyrics and album cover arts. This paper critically
reviews the various approaches adopted in Music Information Processing and
Retrieval and highlights how multimodal algorithms can help Music Computing
applications. First, we categorize the related literature based on the
application they address. Subsequently, we analyze existing information fusion
approaches, and we conclude with the set of challenges that Music Information
Retrieval and Sound and Music Computing research communities should focus in
the next years
Mosquito Detection with Neural Networks: The Buzz of Deep Learning
Many real-world time-series analysis problems are characterised by scarce
data. Solutions typically rely on hand-crafted features extracted from the time
or frequency domain allied with classification or regression engines which
condition on this (often low-dimensional) feature vector. The huge advances
enjoyed by many application domains in recent years have been fuelled by the
use of deep learning architectures trained on large data sets. This paper
presents an application of deep learning for acoustic event detection in a
challenging, data-scarce, real-world problem. Our candidate challenge is to
accurately detect the presence of a mosquito from its acoustic signature. We
develop convolutional neural networks (CNNs) operating on wavelet
transformations of audio recordings. Furthermore, we interrogate the network's
predictive power by visualising statistics of network-excitatory samples. These
visualisations offer a deep insight into the relative informativeness of
components in the detection problem. We include comparisons with conventional
classifiers, conditioned on both hand-tuned and generic features, to stress the
strength of automatic deep feature learning. Detection is achieved with
performance metrics significantly surpassing those of existing algorithmic
methods, as well as marginally exceeding those attained by individual human
experts.Comment: For data and software related to this paper, see
http://humbug.ac.uk/kiskin2017/. Submitted as a conference paper to ECML 201
A Deep Representation for Invariance And Music Classification
Representations in the auditory cortex might be based on mechanisms similar
to the visual ventral stream; modules for building invariance to
transformations and multiple layers for compositionality and selectivity. In
this paper we propose the use of such computational modules for extracting
invariant and discriminative audio representations. Building on a theory of
invariance in hierarchical architectures, we propose a novel, mid-level
representation for acoustical signals, using the empirical distributions of
projections on a set of templates and their transformations. Under the
assumption that, by construction, this dictionary of templates is composed from
similar classes, and samples the orbit of variance-inducing signal
transformations (such as shift and scale), the resulting signature is
theoretically guaranteed to be unique, invariant to transformations and stable
to deformations. Modules of projection and pooling can then constitute layers
of deep networks, for learning composite representations. We present the main
theoretical and computational aspects of a framework for unsupervised learning
of invariant audio representations, empirically evaluated on music genre
classification.Comment: 5 pages, CBMM Memo No. 002, (to appear) IEEE 2014 International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014
Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease
In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders
A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design
Audio fingerprinting, also named as audio hashing, has been well-known as a
powerful technique to perform audio identification and synchronization. It
basically involves two major steps: fingerprint (voice pattern) design and
matching search. While the first step concerns the derivation of a robust and
compact audio signature, the second step usually requires knowledge about
database and quick-search algorithms. Though this technique offers a wide range
of real-world applications, to the best of the authors' knowledge, a
comprehensive survey of existing algorithms appeared more than eight years ago.
Thus, in this paper, we present a more up-to-date review and, for emphasizing
on the audio signal processing aspect, we focus our state-of-the-art survey on
the fingerprint design step for which various audio features and their
tractable statistical models are discussed.Comment: http://www.iaria.org/conferences2015/PATTERNS15.html ; Seventh
International Conferences on Pervasive Patterns and Applications (PATTERNS
2015), Mar 2015, Nice, Franc
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