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Found 19 papers, 15 papers with code
E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks
Sounds carry an abundance of information about activities and events in our everyday environment, such as traffic noise, road works, music, or people talking.
Adapting Language-Audio Models as Few-Shot Audio Learners
We presented the Treff adapter, a training-efficient adapter for CLAP, to boost zero-shot classification performance by making use of a small set of labelled data.
WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research
To address this data scarcity issue, we introduce WavCaps, the first large-scale weakly-labelled audio captioning dataset, comprising approximately 400k audio clips with paired captions.
Leveraging Pre-trained AudioLDM for Text to Sound Generation: A Benchmark Study
Deep neural networks have recently achieved breakthroughs in sound generation with text prompts.
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
By learning the latent representations of audio signals and their compositions without modeling the cross-modal relationship, AudioLDM is advantageous in both generation quality and computational efficiency.
Ranked #4 on
Audio Generation
on AudioCaps
ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech
Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples.
Ontology-aware Learning and Evaluation for Audio Tagging
The proposed metric, ontology-aware mean average precision (OmAP) addresses the weaknesses of mAP by utilizing the AudioSet ontology information during the evaluation.
Visually-Aware Audio Captioning With Adaptive Audio-Visual Attention
Audio captioning aims to generate text descriptions of audio clips.
Learning the Spectrogram Temporal Resolution for Audio Classification
Starting from a high-temporal-resolution spectrogram such as one-millisecond hop size, we show that DiffRes can improve classification accuracy with the same computational complexity.
Simple Pooling Front-ends For Efficient Audio Classification
Recently, there has been increasing interest in building efficient audio neural networks for on-device scenarios.
Segment-level Metric Learning for Few-shot Bioacoustic Event Detection
In addition, we use transductive inference on the validation set during training for better adaptation to novel classes.
BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis
Combining this novel perspective of two-stage synthesis with advanced generative models (i. e., the diffusion models), the proposed BinauralGrad is able to generate accurate and high-fidelity binaural audio samples.
NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality
In this paper, we answer these questions by first defining the human-level quality based on the statistical significance of subjective measure and introducing appropriate guidelines to judge it, and then developing a TTS system called NaturalSpeech that achieves human-level quality on a benchmark dataset.
Ranked #1 on
Text-To-Speech Synthesis
on LJSpeech
VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration
Speech restoration aims to remove distortions in speech signals.
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").
Neural Vocoder is All You Need for Speech Super-resolution
In this paper, we propose a neural vocoder based speech super-resolution method (NVSR) that can handle a variety of input resolution and upsampling ratios.
Ranked #2 on
Audio Super-Resolution
on VCTK Multi-Speaker
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.
CWS-PResUNet: Music Source Separation with Channel-wise Subband Phase-aware ResUNet
On the MUSDB18HQ test set, we propose a 276-layer CWS-PResUNet and achieve state-of-the-art (SoTA) performance on vocals with an 8. 92 signal-to-distortion ratio (SDR) score.
Ranked #10 on
Music Source Separation
on MUSDB18
Channel-wise Subband Input for Better Voice and Accompaniment Separation on High Resolution Music
We aim to address the major issues in CNN-based high-resolution MSS model: high computational cost and weight sharing between distinctly different bands.
Audio and Speech Processing Sound
