Search Results for author:
Found 37 papers, 22 papers with code
Automated Audio Captioning: an Overview of Recent Progress and New Challenges
In this paper, we present a comprehensive review of the published contributions in automated audio captioning, from a variety of existing approaches to evaluation metrics and datasets.
On Metric Learning for Audio-Text Cross-Modal Retrieval
We present an extensive evaluation of popular metric learning objectives on the AudioCaps and Clotho datasets.
Separate What You Describe: Language-Queried Audio Source Separation
In this paper, we introduce the task of language-queried audio source separation (LASS), which aims to separate a target source from an audio mixture based on a natural language query of the target source (e. g., "a man tells a joke followed by people laughing").
Deep Neural Decision Forest for Acoustic Scene Classification
In this paper, we propose a novel approach for ASC using deep neural decision forest (DNDF).
Leveraging Pre-trained BERT for Audio Captioning
BERT is a pre-trained language model that has been extensively used in Natural Language Processing (NLP) tasks.
Local Information Assisted Attention-free Decoder for Audio Captioning
Automated audio captioning (AAC) aims to describe audio data with captions using natural language.
One to Multiple Mapping Dual Learning: Learning Multiple Sources from One Mixed Signal
Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor.
Diverse Audio Captioning via Adversarial Training
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.
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network
A potential approach to this problem is to design an end-to-end method by using a dual generative adversarial network (DualGAN) without dimension reduction of passing information, but it cannot realize one-to-one signal-to-signal translation (see Fig. 1 (a) and (b)).
ARCA23K: An audio dataset for investigating open-set label noise
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.
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement Learning
Automated audio captioning aims to use natural language to describe the content of audio data.
Audio Captioning Transformer
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.
Ranked #2 on
Audio captioning
on AudioCaps
CL4AC: A Contrastive Loss for Audio Captioning
Automated Audio captioning (AAC) is a cross-modal translation task that aims to use natural language to describe the content of an audio clip.
Conditional Sound Generation Using Neural Discrete Time-Frequency Representation Learning
We evaluate our approach on the UrbanSound8K dataset, compared to SampleRNN, with the performance metrics measuring the quality and diversity of generated sounds.
Low-dimensional Denoising Embedding Transformer for ECG Classification
However, the high-dimensional embedding obtained via 1-D convolution and positional encoding can lead to the loss of the signal's own temporal information and a large amount of training parameters.
SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification
In this paper, we present SpecAugment++, a novel data augmentation method for deep neural networks based acoustic scene classification (ASC).
Time-domain Speech Enhancement with Generative Adversarial Learning
Speech enhancement aims to obtain speech signals with high intelligibility and quality from noisy speech.
Enhancing Audio Augmentation Methods with Consistency Learning
For tasks such as classification, there is a good case for learning representations of the data that are invariant to such transformations, yet this is not explicitly enforced by classification losses such as the cross-entropy loss.
An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection
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
In this paper, a novel event-independent network for polyphonic sound event localization and detection is proposed.
Audio and Speech Processing Sound
Evolving Multi-Resolution Pooling CNN for Monaural Singing Voice Separation
Monaural Singing Voice Separation (MSVS) is a challenging task and has been studied for decades.
Learning with Out-of-Distribution Data for Audio Classification
The proposed method uses an auxiliary classifier, trained on data that is known to be in-distribution, for detection and relabelling.
Environmental Sound Classification with Parallel Temporal-spectral Attention
Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC).
IENet: Interacting Embranchment One Stage Anchor Free Detector for Orientation Aerial Object Detection
First, a novel geometric transformation is employed to better represent the oriented object in angle prediction, then a branch interactive module with a self-attention mechanism is developed to fuse features from classification and box regression branches.
Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks
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.
Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy
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
Bayesian inference for PCA and MUSIC algorithms with unknown number of sources
We then use Bayesian method to, for the first time, compute the MAP estimate for the number of sources in PCA and MUSIC algorithms.
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.
Sound Audio and Speech Processing
A joint separation-classification model for sound event detection of weakly labelled data
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
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
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.
Sound Audio and Speech Processing
Attention and Localization based on a Deep Convolutional Recurrent Model for Weakly Supervised Audio Tagging
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
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.
A Joint Detection-Classification Model for Audio Tagging of Weakly Labelled Data
The labeling of an audio clip is often based on the audio events in the clip and no event level label is provided to the user.
Sound
Hierarchical learning for DNN-based acoustic scene classification
In this paper, we present a deep neural network (DNN)-based acoustic scene classification framework.
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
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.
Fully DNN-based Multi-label regression for audio tagging
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.
