Approximate message passing (AMP) algorithms have shown great promise in sparse signal reconstruction due to their low computational requirements and fast convergence to an exact solution. Moreover, they provide a probabilistic framework that is often more intuitive than alternatives such as convex optimisation. In this paper, AMP is used for audio source separation from underdetermined instantaneous mixtures. In the time-frequency domain, it is typical to assume a priori that the sources are sparse, so we solve the corresponding sparse linear inverse problem using AMP. We present a block-based approach that uses AMP to process multiple time-frequency points simultaneously. Two algorithms known as AMP and vector AMP (VAMP) are evaluated in particular. Results show that they are promising in terms of artefact suppression.
The detection of acoustic scenes is a challenging
problem in which environmental sound events must be detected
from a given audio signal. This includes classifying the events as
well as estimating their onset and offset times. We approach this
problem with a neural network architecture that uses the recentlyproposed
capsule routing mechanism. A capsule is a group of
activation units representing a set of properties for an entity of
interest, and the purpose of routing is to identify part-whole
relationships between capsules. That is, a capsule in one layer is
assumed to belong to a capsule in the layer above in terms of the
entity being represented. Using capsule routing, we wish to train
a network that can learn global coherence implicitly, thereby
improving generalization performance. Our proposed method is
evaluated on Task 4 of the DCASE 2017 challenge. Results show
that classification performance is state-of-the-art, achieving an Fscore
of 58.6%. In addition, overfitting is reduced considerably
compared to other architectures.
The Detection and Classi?cation of Acoustic Scenes and Events (DCASE) consists of ?ve audio classi?cation and sound event detectiontasks: 1)Acousticsceneclassi?cation,2)General-purposeaudio tagging of Freesound, 3) Bird audio detection, 4) Weakly-labeled semi-supervised sound event detection and 5) Multi-channel audio classi?cation. In this paper, we create a cross-task baseline system for all ?ve 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 con?guration 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 andanareaunderthecurve(AUC)of0.854onTask3,asoundevent detectionF1scoreof20.8%onTask4,andanF1scoreof87.75%on Task 5. We released the Python source code of the baseline systems under the MIT license for further research.
General-purpose audio tagging refers to classifying sounds that are
of a diverse nature, and is relevant in many applications where
domain-specific information cannot be exploited. The DCASE 2018
challenge introduces Task 2 for this very problem. In this task, there
are a large number of classes and the audio clips vary in duration.
Moreover, a subset of the labels are noisy. In this paper, we propose
a system to address these challenges. The basis of our system is
an ensemble of convolutional neural networks trained on log-scaled
mel spectrograms. We use preprocessing and data augmentation
methods to improve the performance further. To reduce the effects
of label noise, two techniques are proposed: loss function weighting
and pseudo-labeling. Experiments on the private test set of this task
show that our system achieves state-of-the-art performance with a
mean average precision score of 0.951
Kong Qiuqiang, Xu Yong, Iqbal Turab, Cao Yin, Wang Wenwu, Plumbley Mark D. (2019) Acoustic scene generation with conditional SampleRNN,Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019)
Institute of Electrical and Electronics Engineers (IEEE)
Acoustic scene generation (ASG) is a task to generate waveforms
for acoustic scenes. ASG can be used to generate audio
scenes for movies and computer games. Recently, neural networks
such as SampleRNN have been used for speech and
music generation. However, ASG is more challenging due to
its wide variety. In addition, evaluating a generative model is
also difficult. In this paper, we propose to use a conditional
SampleRNN model to generate acoustic scenes conditioned on
the input classes. We also propose objective criteria to evaluate
the quality and diversity of the generated samples based on
classification accuracy. The experiments on the DCASE 2016
Task 1 acoustic scene data show that with the generated audio
samples, a classification accuracy of 65:5% can be achieved
compared to samples generated by a random model of 6:7%
and samples from real recording of 83:1%. The performance
of a classifier trained only on generated samples achieves an
accuracy of 51:3%, as opposed to an accuracy of 6:7% with
samples generated by a random model.
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 modelled 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 examples and the quality of labels of each sound class.
Sound event detection (SED) and localization refer to recognizing
sound events and estimating their spatial and temporal locations.
Using neural networks has become the prevailing method for SED.
In the area of sound localization, which is usually performed by estimating
the direction of arrival (DOA), learning-based methods have
recently been developed. In this paper, it is experimentally shown
that the trained SED model is able to contribute to the direction
of arrival estimation (DOAE). However, joint training of SED and
DOAE degrades the performance of both. Based on these results, a
two-stage polyphonic sound event detection and localization method
is proposed. The method learns SED first, after which the learned
feature layers are transferred for DOAE. It then uses the SED ground
truth as a mask to train DOAE. The proposed method is evaluated on
the DCASE 2019 Task 3 dataset, which contains different overlapping
sound events in different environments. Experimental results
show that the proposed method is able to improve the performance
of both SED and DOAE, and also performs significantly better than
the baseline method.
In supervised machine learning, the assumption that training data is labelled correctly is not always satisfied. In this paper, we investigate an instance of labelling error for classification tasks in which the dataset is corrupted with out-of-distribution
(OOD) instances: data that does not belong to any of the target classes, but is labelled as such. We show that detecting and relabelling certain OOD instances, rather than discarding them, can have a positive effect on learning. The proposed method uses an auxiliary classifier, trained on data that is known to be
in-distribution, for detection and relabelling. The amount of data required for this is shown to be small. Experiments are carried out on the FSDnoisy18k audio dataset, where OOD instances are very prevalent. The proposed method is shown to improve the performance of convolutional neural networks by a significant margin. Comparisons with other noise-robust techniques are similarly encouraging.
Situated in the domain of urban sound scene classi?cation by humans and machines, this research is the ?rst step towards mapping urban noise pollution experienced indoors and ?nding ways to reduce its negative impact in peoples? homes. We have recorded a sound dataset, called Open-Window, which contains recordings from three different locations and four different window states; two stationary states (open and close) and two transitional states (open to close and close to open). We have then built our machine recognition base lines for different scenarios (open set versus closed set) using a deep learning framework. The human listening test is also performed to be able to compare the human and machine performance for detecting the window state just using the acoustic cues. Our experimental results reveal that when using a simple machine baseline system, humans and machines are achieving similar average performance for closed set experiments.