17,581 research outputs found
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
Objects that Sound
In this paper our objectives are, first, networks that can embed audio and
visual inputs into a common space that is suitable for cross-modal retrieval;
and second, a network that can localize the object that sounds in an image,
given the audio signal. We achieve both these objectives by training from
unlabelled video using only audio-visual correspondence (AVC) as the objective
function. This is a form of cross-modal self-supervision from video.
To this end, we design new network architectures that can be trained for
cross-modal retrieval and localizing the sound source in an image, by using the
AVC task. We make the following contributions: (i) show that audio and visual
embeddings can be learnt that enable both within-mode (e.g. audio-to-audio) and
between-mode retrieval; (ii) explore various architectures for the AVC task,
including those for the visual stream that ingest a single image, or multiple
images, or a single image and multi-frame optical flow; (iii) show that the
semantic object that sounds within an image can be localized (using only the
sound, no motion or flow information); and (iv) give a cautionary tale on how
to avoid undesirable shortcuts in the data preparation.Comment: Appears in: European Conference on Computer Vision (ECCV) 201
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
Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression
This paper addresses the problem of localizing audio sources using binaural
measurements. We propose a supervised formulation that simultaneously localizes
multiple sources at different locations. The approach is intrinsically
efficient because, contrary to prior work, it relies neither on source
separation, nor on monaural segregation. The method starts with a training
stage that establishes a locally-linear Gaussian regression model between the
directional coordinates of all the sources and the auditory features extracted
from binaural measurements. While fixed-length wide-spectrum sounds (white
noise) are used for training to reliably estimate the model parameters, we show
that the testing (localization) can be extended to variable-length
sparse-spectrum sounds (such as speech), thus enabling a wide range of
realistic applications. Indeed, we demonstrate that the method can be used for
audio-visual fusion, namely to map speech signals onto images and hence to
spatially align the audio and visual modalities, thus enabling to discriminate
between speaking and non-speaking faces. We release a novel corpus of real-room
recordings that allow quantitative evaluation of the co-localization method in
the presence of one or two sound sources. Experiments demonstrate increased
accuracy and speed relative to several state-of-the-art methods.Comment: 15 pages, 8 figure
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