11,081 research outputs found
Surrey-cvssp system for DCASE2017 challenge task4
In this technique report, we present a bunch of methods for the task 4 of
Detection and Classification of Acoustic Scenes and Events 2017 (DCASE2017)
challenge. This task evaluates systems for the large-scale detection of sound
events using weakly labeled training data. The data are YouTube video excerpts
focusing on transportation and warnings due to their industry applications.
There are two tasks, audio tagging and sound event detection from weakly
labeled data. Convolutional neural network (CNN) and gated recurrent unit (GRU)
based recurrent neural network (RNN) are adopted as our basic framework. We
proposed a learnable gating activation function for selecting informative local
features. Attention-based scheme is used for localizing the specific events in
a weakly-supervised mode. A new batch-level balancing strategy is also proposed
to tackle the data unbalancing problem. Fusion of posteriors from different
systems are found effective to improve the performance. In a summary, we get
61% F-value for the audio tagging subtask and 0.73 error rate (ER) for the
sound event detection subtask on the development set. While the official
multilayer perceptron (MLP) based baseline just obtained 13.1% F-value for the
audio tagging and 1.02 for the sound event detection.Comment: DCASE2017 challenge ranked 1st system, task4, tech repor
A joint separation-classification model for sound event detection of weakly labelled data
Source separation (SS) aims to separate individual sources from an audio
recording. Sound event detection (SED) aims to detect sound events from an
audio recording. We propose a joint separation-classification (JSC) model
trained only on weakly labelled audio data, that is, only the tags of an audio
recording are known but the time of the events are unknown. First, we propose a
separation mapping from the time-frequency (T-F) representation of an audio to
the T-F segmentation masks of the audio events. Second, a classification
mapping is built from each T-F segmentation mask to the presence probability of
each audio event. In the source separation stage, sources of audio events and
time of sound events can be obtained from the T-F segmentation masks. The
proposed method achieves an equal error rate (EER) of 0.14 in SED,
outperforming deep neural network baseline of 0.29. Source separation SDR of
8.08 dB is obtained by using global weighted rank pooling (GWRP) as probability
mapping, outperforming the global max pooling (GMP) based probability mapping
giving SDR at 0.03 dB. Source code of our work is published.Comment: Accepted by ICASSP 201
Large-scale weakly supervised audio classification using gated convolutional neural network
In this paper, we present a gated convolutional neural network and a temporal
attention-based localization method for audio classification, which won the 1st
place in the large-scale weakly supervised sound event detection task of
Detection and Classification of Acoustic Scenes and Events (DCASE) 2017
challenge. The audio clips in this task, which are extracted from YouTube
videos, are manually labeled with one or a few audio tags but without
timestamps of the audio events, which is called as weakly labeled data. Two
sub-tasks are defined in this challenge including audio tagging and sound event
detection using this weakly labeled data. A convolutional recurrent neural
network (CRNN) with learnable gated linear units (GLUs) non-linearity applied
on the log Mel spectrogram is proposed. In addition, a temporal attention
method is proposed along the frames to predicate the locations of each audio
event in a chunk from the weakly labeled data. We ranked the 1st and the 2nd as
a team in these two sub-tasks of DCASE 2017 challenge with F value 55.6\% and
Equal error 0.73, respectively.Comment: submitted to ICASSP2018, summary on the 1st place system in DCASE2017
task4 challeng
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