8,334 research outputs found
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs
Data augmentation has proven to be a promising prospect in improving the
performance of deep learning models by adding variability to training data. In
previous work with developing a noise robust acoustic-to-articulatory speech
inversion system, we have shown the importance of noise augmentation to improve
the performance of speech inversion in noisy speech. In this work, we compare
and contrast different ways of doing data augmentation and show how this
technique improves the performance of articulatory speech inversion not only on
noisy speech, but also on clean speech data. We also propose a Bidirectional
Gated Recurrent Neural Network as the speech inversion system instead of the
previously used feed forward neural network. The inversion system uses
mel-frequency cepstral coefficients (MFCCs) as the input acoustic features and
six vocal tract-variables (TVs) as the output articulatory features. The
Performance of the system was measured by computing the correlation between
estimated and actual TVs on the U. Wisc. X-ray Microbeam database. The proposed
speech inversion system shows a 5% relative improvement in correlation over the
baseline noise robust system for clean speech data. The pre-trained model, when
adapted to each unseen speaker in the test set, improves the average
correlation by another 6%.Comment: EUSIPCO 202
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
ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning
Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species
Robust Speech Recognition Using Generative Adversarial Networks
This paper describes a general, scalable, end-to-end framework that uses the
generative adversarial network (GAN) objective to enable robust speech
recognition. Encoders trained with the proposed approach enjoy improved
invariance by learning to map noisy audio to the same embedding space as that
of clean audio. Unlike previous methods, the new framework does not rely on
domain expertise or simplifying assumptions as are often needed in signal
processing, and directly encourages robustness in a data-driven way. We show
the new approach improves simulated far-field speech recognition of vanilla
sequence-to-sequence models without specialized front-ends or preprocessing
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