Search Results for author: Mark D. Plumbley

Found 31 papers, 19 papers with code

Diverse Audio Captioning via Adversarial Training

no code implementations13 Oct 2021 Xinhao Mei, Xubo Liu, Jianyuan Sun, Mark D. Plumbley, Wenwu Wang

As different people may describe an audio clip from different aspects using distinct words and grammars, we argue that an audio captioning system should have the ability to generate diverse captions for a fixed audio clip and across similar audio clips.

Audio captioning

ARCA23K: An audio dataset for investigating open-set label noise

1 code implementation19 Sep 2021 Turab Iqbal, Yin Cao, Andrew Bailey, Mark D. Plumbley, Wenwu Wang

We show that the majority of labelling errors in ARCA23K are due to out-of-vocabulary audio clips, and we refer to this type of label noise as open-set label noise.

Representation Learning

Audio Captioning Transformer

no code implementations21 Jul 2021 Xinhao Mei, Xubo Liu, Qiushi Huang, Mark D. Plumbley, Wenwu Wang

In this paper, we propose an Audio Captioning Transformer (ACT), which is a full Transformer network based on an encoder-decoder architecture and is totally convolution-free.

Audio captioning

CL4AC: A Contrastive Loss for Audio Captioning

2 code implementations21 Jul 2021 Xubo Liu, Qiushi Huang, Xinhao Mei, Tom Ko, H Lilian Tang, Mark D. Plumbley, Wenwu Wang

Automated Audio captioning (AAC) is a cross-modal translation task that aims to use natural language to describe the content of an audio clip.

Audio captioning Translation

Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning

1 code implementation21 Jul 2021 Xubo Liu, Turab Iqbal, Jinzheng Zhao, Qiushi Huang, Mark D. Plumbley, Wenwu Wang

We evaluate our approach on the UrbanSound8K dataset, compared to SampleRNN, with the performance metrics measuring the quality and diversity of generated sounds.

Music Generation Representation Learning +1

Sound Event Detection: A Tutorial

no code implementations12 Jul 2021 Annamaria Mesaros, Toni Heittola, Tuomas Virtanen, Mark D. Plumbley

The goal of automatic sound event detection (SED) methods is to recognize what is happening in an audio signal and when it is happening.

Event Detection Sound Event Detection

Gender Bias in Depression Detection Using Audio Features

2 code implementations28 Oct 2020 Andrew Bailey, Mark D. Plumbley

Depression is a large-scale mental health problem and a challenging area for machine learning researchers in detection of depression.

Depression Detection

An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection

1 code implementation25 Oct 2020 Yin Cao, Turab Iqbal, Qiuqiang Kong, Fengyan An, Wenwu Wang, Mark D. Plumbley

Polyphonic sound event localization and detection (SELD), which jointly performs sound event detection (SED) and direction-of-arrival (DoA) estimation, detects the type and occurrence time of sound events as well as their corresponding DoA angles simultaneously.

Sound Audio and Speech Processing

Event-Independent Network for Polyphonic Sound Event Localization and Detection

1 code implementation30 Sep 2020 Yin Cao, Turab Iqbal, Qiuqiang Kong, Yue Zhong, Wenwu Wang, Mark D. Plumbley

In this paper, a novel event-independent network for polyphonic sound event localization and detection is proposed.

Audio and Speech Processing Sound

Learning with Out-of-Distribution Data for Audio Classification

1 code implementation11 Feb 2020 Turab Iqbal, Yin Cao, Qiuqiang Kong, Mark D. Plumbley, Wenwu Wang

The proposed method uses an auxiliary classifier, trained on data that is known to be in-distribution, for detection and relabelling.

Audio Classification Classification +1

Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separation

no code implementations21 Oct 2019 Emad M. Grais, Fei Zhao, Mark D. Plumbley

In the spectrogram of a mixture of singing voices and music signals, there is usually more information about the voice in the low frequency bands than the high frequency bands.

Dimensionality Reduction

Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks

1 code implementation14 Jun 2019 Qiuqiang Kong, Yong Xu, Wenwu Wang, Philip J. B. Jackson, Mark D. Plumbley

Single-channel signal separation and deconvolution aims to separate and deconvolve individual sources from a single-channel mixture and is a challenging problem in which no prior knowledge of the mixing filters is available.

Image Inpainting

Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy

1 code implementation1 May 2019 Yin Cao, Qiuqiang Kong, Turab Iqbal, Fengyan An, Wenwu Wang, Mark D. Plumbley

In this paper, it is experimentally shown that the training information of SED is able to contribute to the direction of arrival estimation (DOAE).

Sound Audio and Speech Processing

Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks

no code implementations1 Nov 2018 Emad M. Grais, Hagen Wierstorf, Dominic Ward, Russell Mason, Mark D. Plumbley

Current performance evaluation for audio source separation depends on comparing the processed or separated signals with reference signals.

Audio Source Separation

Sound Event Detection and Time-Frequency Segmentation from Weakly Labelled Data

2 code implementations12 Apr 2018 Qiuqiang Kong, Yong Xu, Iwona Sobieraj, Wenwu Wang, Mark D. Plumbley

Sound event detection (SED) aims to detect when and recognize what sound events happen in an audio clip.

Sound Audio and Speech Processing

Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders

no code implementations2 Mar 2018 Emad M. Grais, Dominic Ward, Mark D. Plumbley

Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals.

Audio Source Separation

A joint separation-classification model for sound event detection of weakly labelled data

2 code implementations8 Nov 2017 Qiuqiang Kong, Yong Xu, Wenwu Wang, Mark D. Plumbley

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.

Sound Audio and Speech Processing

Audio Set classification with attention model: A probabilistic perspective

5 code implementations2 Nov 2017 Qiuqiang Kong, Yong Xu, Wenwu Wang, Mark D. Plumbley

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.

Sound Audio and Speech Processing

Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation

no code implementations28 Oct 2017 Emad M. Grais, Hagen Wierstorf, Dominic Ward, Mark D. Plumbley

In deep neural networks with convolutional layers, each layer typically has fixed-size/single-resolution receptive field (RF).

Audio Source Separation

Large-scale weakly supervised audio classification using gated convolutional neural network

4 code implementations1 Oct 2017 Yong Xu, Qiuqiang Kong, Wenwu Wang, Mark D. Plumbley

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.

Sound Audio and Speech Processing

Single Channel Audio Source Separation using Convolutional Denoising Autoencoders

4 code implementations23 Mar 2017 Emad M. Grais, Mark D. Plumbley

Each CDAE is trained to separate one source and treats the other sources as background noise.

Sound 68T01 H.5.5; I.5; I.2.6; I.4.3

Attention and Localization based on a Deep Convolutional Recurrent Model for Weakly Supervised Audio Tagging

2 code implementations17 Mar 2017 Yong Xu, Qiuqiang Kong, Qiang Huang, Wenwu Wang, Mark D. Plumbley

Audio tagging aims to perform multi-label classification on audio chunks and it is a newly proposed task in the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE 2016) challenge.

Sound

Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging

2 code implementations24 Feb 2017 Yong Xu, Qiuqiang Kong, Qiang Huang, Wenwu Wang, Mark D. Plumbley

In this paper, we propose to use a convolutional neural network (CNN) to extract robust features from mel-filter banks (MFBs), spectrograms or even raw waveforms for audio tagging.

Audio Tagging

Automatic Environmental Sound Recognition: Performance versus Computational Cost

no code implementations15 Jul 2016 Siddharth Sigtia, Adam M. Stark, Sacha Krstulovic, Mark D. Plumbley

In the context of the Internet of Things (IoT), sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard constraints on the available computing power.

Classification General Classification

Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging

2 code implementations13 Jul 2016 Yong Xu, Qiang Huang, Wenwu Wang, Peter Foster, Siddharth Sigtia, Philip J. B. Jackson, Mark D. Plumbley

For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep de-noising auto-encoder (sDAE or aDAE) to generate new data-driven features from the Mel-Filter Banks (MFBs) features.

Audio Tagging General Classification +1

Fully DNN-based Multi-label regression for audio tagging

no code implementations24 Jun 2016 Yong Xu, Qiang Huang, Wenwu Wang, Philip J. B. Jackson, Mark D. Plumbley

Compared with the conventional Gaussian Mixture Model (GMM) and support vector machine (SVM) methods, the proposed fully DNN-based method could well utilize the long-term temporal information with the whole chunk as the input.

Audio Tagging Event Detection +2

Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network

1 code implementation17 Apr 2015 Andrew J. R. Simpson, Gerard Roma, Mark D. Plumbley

Identification and extraction of singing voice from within musical mixtures is a key challenge in source separation and machine audition.

Speech Separation

Acoustic Scene Classification

no code implementations13 Nov 2014 Daniele Barchiesi, Dimitrios Giannoulis, Dan Stowell, Mark D. Plumbley

We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques.

Acoustic Scene Classification Classification +2

Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

no code implementations26 May 2014 Dan Stowell, Mark D. Plumbley

Feature learning can be performed at large scale and "unsupervised", meaning it requires no manual data labelling, yet it can improve performance on "supervised" tasks such as classification.

Classification General Classification

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