1,441 research outputs found
Stacked Convolutional and Recurrent Neural Networks for Bird Audio Detection
This paper studies the detection of bird calls in audio segments using
stacked convolutional and recurrent neural networks. Data augmentation by
blocks mixing and domain adaptation using a novel method of test mixing are
proposed and evaluated in regard to making the method robust to unseen data.
The contributions of two kinds of acoustic features (dominant frequency and log
mel-band energy) and their combinations are studied in the context of bird
audio detection. Our best achieved AUC measure on five cross-validations of the
development data is 95.5% and 88.1% on the unseen evaluation data.Comment: Accepted for European Signal Processing Conference 201
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
DCASE 2018 Challenge Surrey Cross-Task convolutional neural network baseline
The Detection and Classification of Acoustic Scenes and Events (DCASE)
consists of five audio classification and sound event detection tasks: 1)
Acoustic scene classification, 2) General-purpose audio tagging of Freesound,
3) Bird audio detection, 4) Weakly-labeled semi-supervised sound event
detection and 5) Multi-channel audio classification. In this paper, we create a
cross-task baseline system for all five tasks based on a convlutional neural
network (CNN): a "CNN Baseline" system. We implemented CNNs with 4 layers and 8
layers originating from AlexNet and VGG from computer vision. We investigated
how the performance varies from task to task with the same configuration of
neural networks. Experiments show that deeper CNN with 8 layers performs better
than CNN with 4 layers on all tasks except Task 1. Using CNN with 8 layers, we
achieve an accuracy of 0.680 on Task 1, an accuracy of 0.895 and a mean average
precision (MAP) of 0.928 on Task 2, an accuracy of 0.751 and an area under the
curve (AUC) of 0.854 on Task 3, a sound event detection F1 score of 20.8% on
Task 4, and an F1 score of 87.75% on Task 5. We released the Python source code
of the baseline systems under the MIT license for further research.Comment: Accepted by DCASE 2018 Workshop. 4 pages. Source code availabl
Polyphonic Sound Event Detection by using Capsule Neural Networks
Artificial sound event detection (SED) has the aim to mimic the human ability
to perceive and understand what is happening in the surroundings. Nowadays,
Deep Learning offers valuable techniques for this goal such as Convolutional
Neural Networks (CNNs). The Capsule Neural Network (CapsNet) architecture has
been recently introduced in the image processing field with the intent to
overcome some of the known limitations of CNNs, specifically regarding the
scarce robustness to affine transformations (i.e., perspective, size,
orientation) and the detection of overlapped images. This motivated the authors
to employ CapsNets to deal with the polyphonic-SED task, in which multiple
sound events occur simultaneously. Specifically, we propose to exploit the
capsule units to represent a set of distinctive properties for each individual
sound event. Capsule units are connected through a so-called "dynamic routing"
that encourages learning part-whole relationships and improves the detection
performance in a polyphonic context. This paper reports extensive evaluations
carried out on three publicly available datasets, showing how the CapsNet-based
algorithm not only outperforms standard CNNs but also allows to achieve the
best results with respect to the state of the art algorithms
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
Sample Mixed-Based Data Augmentation for Domestic Audio Tagging
Audio tagging has attracted increasing attention since last decade and has
various potential applications in many fields. The objective of audio tagging
is to predict the labels of an audio clip. Recently deep learning methods have
been applied to audio tagging and have achieved state-of-the-art performance,
which provides a poor generalization ability on new data. However due to the
limited size of audio tagging data such as DCASE data, the trained models tend
to result in overfitting of the network. Previous data augmentation methods
such as pitch shifting, time stretching and adding background noise do not show
much improvement in audio tagging. In this paper, we explore the sample mixed
data augmentation for the domestic audio tagging task, including mixup,
SamplePairing and extrapolation. We apply a convolutional recurrent neural
network (CRNN) with attention module with log-scaled mel spectrum as a baseline
system. In our experiments, we achieve an state-of-the-art of equal error rate
(EER) of 0.10 on DCASE 2016 task4 dataset with mixup approach, outperforming
the baseline system without data augmentation.Comment: submitted to the workshop of Detection and Classification of Acoustic
Scenes and Events 2018 (DCASE 2018), 19-20 November 2018, Surrey, U
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