1,923 research outputs found
Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events
In this paper, we propose a new strategy for acoustic scene classification
(ASC) , namely recognizing acoustic scenes through identifying distinct sound
events. This differs from existing strategies, which focus on characterizing
global acoustical distributions of audio or the temporal evolution of
short-term audio features, without analysis down to the level of sound events.
To identify distinct sound events for each scene, we formulate ASC in a
multi-instance learning (MIL) framework, where each audio recording is mapped
into a bag-of-instances representation. Here, instances can be seen as
high-level representations for sound events inside a scene. We also propose a
MIL neural networks model, which implicitly identifies distinct instances
(i.e., sound events). Furthermore, we propose two specially designed modules
that model the multi-temporal scale and multi-modal natures of the sound events
respectively. The experiments were conducted on the official development set of
the DCASE2018 Task1 Subtask B, and our best-performing model improves over the
official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy.
This study indicates that recognizing acoustic scenes by identifying distinct
sound events is effective and paves the way for future studies that combine
this strategy with previous ones.Comment: code URL typo, code is available at
https://github.com/hackerekcah/distinct-events-asc.gi
Weakly Labelled AudioSet Tagging with Attention Neural Networks
Audio tagging is the task of predicting the presence or absence of sound
classes within an audio clip. Previous work in audio tagging focused on
relatively small datasets limited to recognising a small number of sound
classes. We investigate audio tagging on AudioSet, which is a dataset
consisting of over 2 million audio clips and 527 classes. AudioSet is weakly
labelled, in that only the presence or absence of sound classes is known for
each clip, while the onset and offset times are unknown. To address the
weakly-labelled audio tagging problem, we propose attention neural networks as
a way to attend the most salient parts of an audio clip. We bridge the
connection between attention neural networks and multiple instance learning
(MIL) methods, and propose decision-level and feature-level attention neural
networks for audio tagging. We investigate attention neural networks modeled by
different functions, depths and widths. Experiments on AudioSet show that the
feature-level attention neural network achieves a state-of-the-art mean average
precision (mAP) of 0.369, outperforming the best multiple instance learning
(MIL) method of 0.317 and Google's deep neural network baseline of 0.314. In
addition, we discover that the audio tagging performance on AudioSet embedding
features has a weak correlation with the number of training samples and the
quality of labels of each sound class.Comment: 13 page
Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes
In this work we propose approaches to effectively transfer knowledge from
weakly labeled web audio data. We first describe a convolutional neural network
(CNN) based framework for sound event detection and classification using weakly
labeled audio data. Our model trains efficiently from audios of variable
lengths; hence, it is well suited for transfer learning. We then propose
methods to learn representations using this model which can be effectively used
for solving the target task. We study both transductive and inductive transfer
learning tasks, showing the effectiveness of our methods for both domain and
task adaptation. We show that the learned representations using the proposed
CNN model generalizes well enough to reach human level accuracy on ESC-50 sound
events dataset and set state of art results on this dataset. We further use
them for acoustic scene classification task and once again show that our
proposed approaches suit well for this task as well. We also show that our
methods are helpful in capturing semantic meanings and relations as well.
Moreover, in this process we also set state-of-art results on Audioset dataset,
relying on balanced training set.Comment: ICASSP 201
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