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A database and challenge for acoustic scene classification and event detection
Exploring convolutional, recurrent, and hybrid deep neural networks for speech and music detection in a large audio dataset
Audio signals represent a wide diversity of acoustic events, from background environmental noise to spoken
communication. Machine learning models such as neural networks have already been proposed for audio signal
modeling, where recurrent structures can take advantage of temporal dependencies. This work aims to study the
implementation of several neural network-based systems for speech and music event detection over a collection of
77,937 10-second audio segments (216 h), selected from the Google AudioSet dataset. These segments belong to
YouTube videos and have been represented as mel-spectrograms. We propose and compare two approaches. The
first one is the training of two different neural networks, one for speech detection and another for music detection.
The second approach consists on training a single neural network to tackle both tasks at the same time. The studied
architectures include fully connected, convolutional and LSTM (long short-term memory) recurrent networks.
Comparative results are provided in terms of classification performance and model complexity. We would like to
highlight the performance of convolutional architectures, specially in combination with an LSTM stage. The hybrid
convolutional-LSTM models achieve the best overall results (85% accuracy) in the three proposed tasks. Furthermore,
a distractor analysis of the results has been carried out in order to identify which events in the ontology are the most
harmful for the performance of the models, showing some difficult scenarios for the detection of music and speechThis work has been supported by project âDSSL: Redes Profundas y Modelos
de Subespacios para Deteccion y Seguimiento de Locutor, Idioma y
Enfermedades Degenerativas a partir de la Vozâ (TEC2015-68172-C2-1-P),
funded by the Ministry of Economy and Competitivity of Spain and FEDE
Automatic Environmental Sound Recognition: Performance versus Computational Cost
In the context of the Internet of Things (IoT), sound sensing applications
are required to run on embedded platforms where notions of product pricing and
form factor impose hard constraints on the available computing power. Whereas
Automatic Environmental Sound Recognition (AESR) algorithms are most often
developed with limited consideration for computational cost, this article seeks
which AESR algorithm can make the most of a limited amount of computing power
by comparing the sound classification performance em as a function of its
computational cost. Results suggest that Deep Neural Networks yield the best
ratio of sound classification accuracy across a range of computational costs,
while Gaussian Mixture Models offer a reasonable accuracy at a consistently
small cost, and Support Vector Machines stand between both in terms of
compromise between accuracy and computational cost
Joint model-based recognition and localization of overlapped acoustic events using a set of distributed small microphone arrays
In the analysis of acoustic scenes, often the occurring sounds have to be
detected in time, recognized, and localized in space. Usually, each of these
tasks is done separately. In this paper, a model-based approach to jointly
carry them out for the case of multiple simultaneous sources is presented and
tested. The recognized event classes and their respective room positions are
obtained with a single system that maximizes the combination of a large set of
scores, each one resulting from a different acoustic event model and a
different beamformer output signal, which comes from one of several
arbitrarily-located small microphone arrays. By using a two-step method, the
experimental work for a specific scenario consisting of meeting-room acoustic
events, either isolated or overlapped with speech, is reported. Tests carried
out with two datasets show the advantage of the proposed approach with respect
to some usual techniques, and that the inclusion of estimated priors brings a
further performance improvement.Comment: Computational acoustic scene analysis, microphone array signal
processing, acoustic event detectio
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Sound events often occur in unstructured environments where they exhibit wide
variations in their frequency content and temporal structure. Convolutional
neural networks (CNN) are able to extract higher level features that are
invariant to local spectral and temporal variations. Recurrent neural networks
(RNNs) are powerful in learning the longer term temporal context in the audio
signals. CNNs and RNNs as classifiers have recently shown improved performances
over established methods in various sound recognition tasks. We combine these
two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it
on a polyphonic sound event detection task. We compare the performance of the
proposed CRNN method with CNN, RNN, and other established methods, and observe
a considerable improvement for four different datasets consisting of everyday
sound events.Comment: Accepted for IEEE Transactions on Audio, Speech and Language
Processing, Special Issue on Sound Scene and Event Analysi
CNN Architectures for Large-Scale Audio Classification
Convolutional Neural Networks (CNNs) have proven very effective in image
classification and show promise for audio. We use various CNN architectures to
classify the soundtracks of a dataset of 70M training videos (5.24 million
hours) with 30,871 video-level labels. We examine fully connected Deep Neural
Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We
investigate varying the size of both training set and label vocabulary, finding
that analogs of the CNNs used in image classification do well on our audio
classification task, and larger training and label sets help up to a point. A
model using embeddings from these classifiers does much better than raw
features on the Audio Set [5] Acoustic Event Detection (AED) classification
task.Comment: Accepted for publication at ICASSP 2017 Changes: Added definitions of
mAP, AUC, and d-prime. Updated mAP/AUC/d-prime numbers for Audio Set based on
changes of latest Audio Set revision. Changed wording to fit 4 page limit
with new addition
Classification of Overlapped Audio Events Based on AT, PLSA, and the Combination of Them
Audio event classification, as an important part of Computational Auditory Scene Analysis, has attracted much attention. Currently, the classification technology is mature enough to classify isolated audio events accurately, but for overlapped audio events, it performs much worse. While in real life, most audio documents would have certain percentage of overlaps, and so the overlap classification problem is an important part of audio classification. Nowadays, the work on overlapped audio event classification is still scarce, and most existing overlap classification systems can only recognize one audio event for an overlap. In this paper, in order to deal with overlaps, we innovatively introduce the author-topic (AT) model which was first proposed for text analysis into audio classification, and innovatively combine it with PLSA (Probabilistic Latent Semantic Analysis). We propose 4 systems, i.e. AT, PLSA, AT-PLSA and PLSA-AT, to classify overlaps. The 4 proposed systems have the ability to recognize two or more audio events for an overlap. The experimental results show that the 4 systems perform well in classifying overlapped audio events, whether it is the overlap in training set or the overlap out of training set. Also they perform well in classifying isolated audio events
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