High Quality Audio Coding with MDCTNet
We propose a neural audio generative model, MDCTNet, operating in the perceptually weighted domain of an adaptive modified discrete cosine transform (MDCT). The architecture of the model captures correlations in both time and frequency directions with recurrent layers (RNNs). An audio coding system is obtained by training MDCTNet on a diverse set of fullband monophonic audio signals at 48 kHz sampling, conditioned by a perceptual audio encoder. In a subjective listening test with ten excerpts chosen to be balanced across content types, yet stressful for both codecs, the mean performance of the proposed system for 24 kb/s variable bitrate (VBR) is similar to that of Opus at twice the bitrate.
Code (0)
등록된 구현이 없습니다.
Tasks
Vocal Bursts Intensity PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Neural Speech and Audio Coding: Modern AI Technology Meets Traditional Codecs
This paper explores the integration of model-based and data-driven approaches within the realm of neural speech and audio coding systems. It highlights the challenges posed by the subjective evaluation processes of speec…
FlowMAC: Conditional Flow Matching for Audio Coding at Low Bit Rates
This paper introduces FlowMAC, a novel neural audio codec for high-quality general audio compression at low bit rates based on conditional flow matching (CFM). FlowMAC jointly learns a mel spectrogram encoder, quantizer …
Audio CompressionCPUDecoderOpenACE: An Open Benchmark for Evaluating Audio Coding Performance
Audio and speech coding lack unified evaluation and open-source testing. Many candidate systems were evaluated on proprietary, non-reproducible, or small data, and machine learning-based codecs are often tested on datase…
Psychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding
Conventional audio coding technologies commonly leverage human perception of sound, or psychoacoustics, to reduce the bitrate while preserving the perceptual quality of the decoded audio signals. For neural audio codecs,…
IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling
Text-to-audio generation synthesizes realistic sounds or music given a natural language prompt. Diffusion-based frameworks, including the Tango and the AudioLDM series, represent the state-of-the-art in text-to-audio gen…
AudioCapsAudio GenerationFAD