4,009 research outputs found
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
Learning Audio Sequence Representations for Acoustic Event Classification
Acoustic Event Classification (AEC) has become a significant task for
machines to perceive the surrounding auditory scene. However, extracting
effective representations that capture the underlying characteristics of the
acoustic events is still challenging. Previous methods mainly focused on
designing the audio features in a 'hand-crafted' manner. Interestingly,
data-learnt features have been recently reported to show better performance. Up
to now, these were only considered on the frame-level. In this paper, we
propose an unsupervised learning framework to learn a vector representation of
an audio sequence for AEC. This framework consists of a Recurrent Neural
Network (RNN) encoder and a RNN decoder, which respectively transforms the
variable-length audio sequence into a fixed-length vector and reconstructs the
input sequence on the generated vector. After training the encoder-decoder, we
feed the audio sequences to the encoder and then take the learnt vectors as the
audio sequence representations. Compared with previous methods, the proposed
method can not only deal with the problem of arbitrary-lengths of audio
streams, but also learn the salient information of the sequence. Extensive
evaluation on a large-size acoustic event database is performed, and the
empirical results demonstrate that the learnt audio sequence representation
yields a significant performance improvement by a large margin compared with
other state-of-the-art hand-crafted sequence features for AEC
Learning sound representations using trainable COPE feature extractors
Sound analysis research has mainly been focused on speech and music
processing. The deployed methodologies are not suitable for analysis of sounds
with varying background noise, in many cases with very low signal-to-noise
ratio (SNR). In this paper, we present a method for the detection of patterns
of interest in audio signals. We propose novel trainable feature extractors,
which we call COPE (Combination of Peaks of Energy). The structure of a COPE
feature extractor is determined using a single prototype sound pattern in an
automatic configuration process, which is a type of representation learning. We
construct a set of COPE feature extractors, configured on a number of training
patterns. Then we take their responses to build feature vectors that we use in
combination with a classifier to detect and classify patterns of interest in
audio signals. We carried out experiments on four public data sets: MIVIA audio
events, MIVIA road events, ESC-10 and TU Dortmund data sets. The results that
we achieved (recognition rate equal to 91.71% on the MIVIA audio events, 94% on
the MIVIA road events, 81.25% on the ESC-10 and 94.27% on the TU Dortmund)
demonstrate the effectiveness of the proposed method and are higher than the
ones obtained by other existing approaches. The COPE feature extractors have
high robustness to variations of SNR. Real-time performance is achieved even
when the value of a large number of features is computed.Comment: Accepted for publication in Pattern Recognitio
Multimodal Speech Emotion Recognition Using Audio and Text
Speech emotion recognition is a challenging task, and extensive reliance has
been placed on models that use audio features in building well-performing
classifiers. In this paper, we propose a novel deep dual recurrent encoder
model that utilizes text data and audio signals simultaneously to obtain a
better understanding of speech data. As emotional dialogue is composed of sound
and spoken content, our model encodes the information from audio and text
sequences using dual recurrent neural networks (RNNs) and then combines the
information from these sources to predict the emotion class. This architecture
analyzes speech data from the signal level to the language level, and it thus
utilizes the information within the data more comprehensively than models that
focus on audio features. Extensive experiments are conducted to investigate the
efficacy and properties of the proposed model. Our proposed model outperforms
previous state-of-the-art methods in assigning data to one of four emotion
categories (i.e., angry, happy, sad and neutral) when the model is applied to
the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.Comment: 7 pages, Accepted as a conference paper at IEEE SLT 201
Polyphonic audio tagging with sequentially labelled data using CRNN with learnable gated linear units
Audio tagging aims to detect the types of sound events occurring in an audio
recording. To tag the polyphonic audio recordings, we propose to use
Connectionist Temporal Classification (CTC) loss function on the top of
Convolutional Recurrent Neural Network (CRNN) with learnable Gated Linear Units
(GLU-CTC), based on a new type of audio label data: Sequentially Labelled Data
(SLD). In GLU-CTC, CTC objective function maps the frame-level probability of
labels to clip-level probability of labels. To compare the mapping ability of
GLU-CTC for sound events, we train a CRNN with GLU based on Global Max Pooling
(GLU-GMP) and a CRNN with GLU based on Global Average Pooling (GLU-GAP). And we
also compare the proposed GLU-CTC system with the baseline system, which is a
CRNN trained using CTC loss function without GLU. The experiments show that the
GLU-CTC achieves an Area Under Curve (AUC) score of 0.882 in audio tagging,
outperforming the GLU-GMP of 0.803, GLU-GAP of 0.766 and baseline system of
0.837. That means based on the same CRNN model with GLU, the performance of CTC
mapping is better than the GMP and GAP mapping. Given both based on the CTC
mapping, the CRNN with GLU outperforms the CRNN without GLU.Comment: DCASE2018 Workshop. arXiv admin note: text overlap with
arXiv:1808.0193
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