7,033 research outputs found
Robust sound event recognition using convolutional neural networks
Traditional sound event recognition methods based on informative front end features such as MFCC, with back end sequencing methods such as HMM, tend to perform poorly in the presence of interfering acoustic noise.
Since noise corruption may be unavoidable in practical situations, it is important to develop more robust features and classifiers. Recent advances in this field use powerful machine learning techniques with high dimensional input features such as spectrograms or auditory image. These improve robustness largely thanks to the discriminative capabilities of the back end classifiers. We extend this further by proposing novel features derived from spectrogram energy triggering, allied with the powerful classification capabilities of a convolutional neural network (CNN). The proposed method demonstrates excellent performance under noise-corrupted conditions when compared against state-of-the-art approaches on standard evaluation tasks. To the author's knowledge this in the first application of CNN in this field
Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging
Environmental audio tagging is a newly proposed task to predict the presence
or absence of a specific audio event in a chunk. Deep neural network (DNN)
based methods have been successfully adopted for predicting the audio tags in
the domestic audio scene. In this paper, we propose to use a convolutional
neural network (CNN) to extract robust features from mel-filter banks (MFBs),
spectrograms or even raw waveforms for audio tagging. Gated recurrent unit
(GRU) based recurrent neural networks (RNNs) are then cascaded to model the
long-term temporal structure of the audio signal. To complement the input
information, an auxiliary CNN is designed to learn on the spatial features of
stereo recordings. We evaluate our proposed methods on Task 4 (audio tagging)
of the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE
2016) challenge. Compared with our recent DNN-based method, the proposed
structure can reduce the equal error rate (EER) from 0.13 to 0.11 on the
development set. The spatial features can further reduce the EER to 0.10. The
performance of the end-to-end learning on raw waveforms is also comparable.
Finally, on the evaluation set, we get the state-of-the-art performance with
0.12 EER while the performance of the best existing system is 0.15 EER.Comment: Accepted to IJCNN2017, Anchorage, Alaska, US
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
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
Musical notes classification with Neuromorphic Auditory System using FPGA and a Convolutional Spiking Network
In this paper, we explore the capabilities of a sound
classification system that combines both a novel FPGA cochlear
model implementation and a bio-inspired technique based on a
trained convolutional spiking network. The neuromorphic
auditory system that is used in this work produces a form of
representation that is analogous to the spike outputs of the
biological cochlea. The auditory system has been developed using
a set of spike-based processing building blocks in the frequency
domain. They form a set of band pass filters in the spike-domain
that splits the audio information in 128 frequency channels, 64
for each of two audio sources. Address Event Representation
(AER) is used to communicate the auditory system with the
convolutional spiking network. A layer of convolutional spiking
network is developed and trained on a computer with the ability
to detect two kinds of sound: artificial pure tones in the presence
of white noise and electronic musical notes. After the training
process, the presented system is able to distinguish the different
sounds in real-time, even in the presence of white noise.Ministerio de Economía y Competitividad TEC2012-37868-C04-0
Robust sound event detection in bioacoustic sensor networks
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs),
can record sounds of wildlife over long periods of time in scalable and
minimally invasive ways. Deriving per-species abundance estimates from these
sensors requires detection, classification, and quantification of animal
vocalizations as individual acoustic events. Yet, variability in ambient noise,
both over time and across sensors, hinders the reliability of current automated
systems for sound event detection (SED), such as convolutional neural networks
(CNN) in the time-frequency domain. In this article, we develop, benchmark, and
combine several machine listening techniques to improve the generalizability of
SED models across heterogeneous acoustic environments. As a case study, we
consider the problem of detecting avian flight calls from a ten-hour recording
of nocturnal bird migration, recorded by a network of six ARUs in the presence
of heterogeneous background noise. Starting from a CNN yielding
state-of-the-art accuracy on this task, we introduce two noise adaptation
techniques, respectively integrating short-term (60 milliseconds) and long-term
(30 minutes) context. First, we apply per-channel energy normalization (PCEN)
in the time-frequency domain, which applies short-term automatic gain control
to every subband in the mel-frequency spectrogram. Secondly, we replace the
last dense layer in the network by a context-adaptive neural network (CA-NN)
layer. Combining them yields state-of-the-art results that are unmatched by
artificial data augmentation alone. We release a pre-trained version of our
best performing system under the name of BirdVoxDetect, a ready-to-use detector
of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019;
revised August 2019; published October 201
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