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Self-supervised speech representation learning: A review
Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …
necessitated the building of specialist models for individual tasks and application scenarios …
A comprehensive survey and analysis of generative models in machine learning
Generative models have been in existence for many decades. In the field of machine
learning, we come across many scenarios when directly learning a target is intractable …
learning, we come across many scenarios when directly learning a target is intractable …
Qwen-audio: Advancing universal audio understanding via unified large-scale audio-language models
[PDF][PDF] Jukebox: A generative model for music
We introduce Jukebox, a model that generates music with singing in the raw audio domain.
We tackle the long context of raw audio using a multiscale VQ-VAE to compress it to discrete …
We tackle the long context of raw audio using a multiscale VQ-VAE to compress it to discrete …
Melgan: Generative adversarial networks for conditional waveform synthesis
Previous works (Donahue et al., 2018a; Engel et al., 2019a) have found that generating
coherent raw audio waveforms with GANs is challenging. In this paper, we show that it is …
coherent raw audio waveforms with GANs is challenging. In this paper, we show that it is …
Mert: Acoustic music understanding model with large-scale self-supervised training
Self-supervised learning (SSL) has recently emerged as a promising paradigm for training
generalisable models on large-scale data in the fields of vision, text, and speech. Although …
generalisable models on large-scale data in the fields of vision, text, and speech. Although …
DDSP: Differentiable digital signal processing
Most generative models of audio directly generate samples in one of two domains: time or
frequency. While sufficient to express any signal, these representations are inefficient, as …
frequency. While sufficient to express any signal, these representations are inefficient, as …