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Found 13 papers, 8 papers with code
BINAQUAL: A Full-Reference Objective Localization Similarity Metric for Binaural Audio
Spatial audio enhances immersion in applications such as virtual reality, augmented reality, gaming, and cinema by creating a three-dimensional auditory experience.
Perceptual Audio Coding: A 40-Year Historical Perspective
In the history of audio and acoustic signal processing, perceptual audio coding has certainly excelled as a bright success story by its ubiquitous deployment in virtually all digital media devices, such as computers, tablets, mobile phones, set-top-boxes, and digital radios.
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.
Using Rater and System Metadata to Explain Variance in the VoiceMOS Challenge 2022 Dataset
Non-reference speech quality models are important for a growing number of applications.
Ultra-Low-Bitrate Speech Coding with Pretrained Transformers
Our numerical experiments show that supplementing the convolutional encoder of a neural speech codec with Transformer speech embeddings yields a speech codec with a bitrate of $600\,\mathrm{bps}$ that outperforms the original neural speech codec in synthesized speech quality when trained at the same bitrate.
A Comparison of Deep Learning MOS Predictors for Speech Synthesis Quality
In this paper, we evaluate several MOS predictors based on wav2vec 2. 0 and the NISQA speech quality prediction model to explore the role of the training data, the influence of the system type, and the role of cross-domain features in SSL models.
SoundStream: An End-to-End Neural Audio Codec
We present SoundStream, a novel neural audio codec that can efficiently compress speech, music and general audio at bitrates normally targeted by speech-tailored codecs.
Handling Background Noise in Neural Speech Generation
Recent advances in neural-network based generative modeling of speech has shown great potential for speech coding.
WARP-Q: Quality Prediction For Generative Neural Speech Codecs
Good speech quality has been achieved using waveform matching and parametric reconstruction coders.
Generative Speech Coding with Predictive Variance Regularization
We introduce predictive-variance regularization to reduce the sensitivity to outliers, resulting in a significant increase in performance.
A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet
We demonstrate that LPCNet operating at 1. 6 kb/s achieves significantly higher quality than MELP and that uncompressed LPCNet can exceed the quality of a waveform codec operating at low bitrate.
LPCNet: Improving Neural Speech Synthesis Through Linear Prediction
We demonstrate that LPCNet can achieve significantly higher quality than WaveRNN for the same network size and that high quality LPCNet speech synthesis is achievable with a complexity under 3 GFLOPS.
Wavenet based low rate speech coding
Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used.
