776 research outputs found
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
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
Audio Set classification with attention model: A probabilistic perspective
This paper investigates the classification of the Audio Set dataset. Audio
Set is a large scale weakly labelled dataset of sound clips. Previous work used
multiple instance learning (MIL) to classify weakly labelled data. In MIL, a
bag consists of several instances, and a bag is labelled positive if at least
one instances in the audio clip is positive. A bag is labelled negative if all
the instances in the bag are negative. We propose an attention model to tackle
the MIL problem and explain this attention model from a novel probabilistic
perspective. We define a probability space on each bag, where each instance in
the bag has a trainable probability measure for each class. Then the
classification of a bag is the expectation of the classification output of the
instances in the bag with respect to the learned probability measure.
Experimental results show that our proposed attention model modeled by fully
connected deep neural network obtains mAP of 0.327 on Audio Set dataset,
outperforming the Google's baseline of 0.314 and recurrent neural network of
0.325.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
Sound Event Detection with Sequentially Labelled Data Based on Connectionist Temporal Classification and Unsupervised Clustering
Sound event detection (SED) methods typically rely on either strongly
labelled data or weakly labelled data. As an alternative, sequentially labelled
data (SLD) was proposed. In SLD, the events and the order of events in audio
clips are known, without knowing the occurrence time of events. This paper
proposes a connectionist temporal classification (CTC) based SED system that
uses SLD instead of strongly labelled data, with a novel unsupervised
clustering stage. Experiments on 41 classes of sound events show that the
proposed two-stage method trained on SLD achieves performance comparable to the
previous state-of-the-art SED system trained on strongly labelled data, and is
far better than another state-of-the-art SED system trained on weakly labelled
data, which indicates the effectiveness of the proposed two-stage method
trained on SLD without any onset/offset time of sound events
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
Joint Detection and Classification Convolutional Neural Network on Weakly Labelled Bird Audio Detection
Bird audio detection (BAD) aims to detect whether there is a bird call in an audio recording or not. One difficulty of this task is that the bird sound datasets are weakly labelled, that is only the presence or absence of a bird in a recording is known, without knowing when the birds call. We propose to apply joint detection and classification (JDC) model on the weakly labelled data (WLD) to detect and classify an audio clip at the same time. First, we apply VGG like convolutional neural network (CNN) on mel spectrogram as baseline. Then we propose a JDC-CNN model with VGG as a classifier and CNN as a detector. We report the denoising method including optimally-modified log-spectral amplitude (OM-LSA), median filter and spectral spectrogram will worse the classification accuracy on the contrary to previous work. JDC-CNN can predict the time stamps of the events from weakly labelled data, so is able to do sound event detection from WLD. We obtained area under curve (AUC) of 95.70% on the development data and 81.36% on the unseen evaluation data, which is nearly comparable to the baseline CNN model
Sound Event Detection and Time-Frequency Segmentation from Weakly Labelled Data
Sound event detection (SED) aims to detect when and recognize what sound
events happen in an audio clip. Many supervised SED algorithms rely on strongly
labelled data which contains the onset and offset annotations of sound events.
However, many audio tagging datasets are weakly labelled, that is, only the
presence of the sound events is known, without knowing their onset and offset
annotations. In this paper, we propose a time-frequency (T-F) segmentation
framework trained on weakly labelled data to tackle the sound event detection
and separation problem. In training, a segmentation mapping is applied on a T-F
representation, such as log mel spectrogram of an audio clip to obtain T-F
segmentation masks of sound events. The T-F segmentation masks can be used for
separating the sound events from the background scenes in the time-frequency
domain. Then a classification mapping is applied on the T-F segmentation masks
to estimate the presence probabilities of the sound events. We model the
segmentation mapping using a convolutional neural network and the
classification mapping using a global weighted rank pooling (GWRP). In SED,
predicted onset and offset times can be obtained from the T-F segmentation
masks. As a byproduct, separated waveforms of sound events can be obtained from
the T-F segmentation masks. We remixed the DCASE 2018 Task 1 acoustic scene
data with the DCASE 2018 Task 2 sound events data. When mixing under 0 dB, the
proposed method achieved F1 scores of 0.534, 0.398 and 0.167 in audio tagging,
frame-wise SED and event-wise SED, outperforming the fully connected deep
neural network baseline of 0.331, 0.237 and 0.120, respectively. In T-F
segmentation, we achieved an F1 score of 0.218, where previous methods were not
able to do T-F segmentation.Comment: 12 pages, 8 figure
- …