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Found 16 papers, 7 papers with code
Efficient Spoken Language Recognition via Multilabel Classification
Spoken language recognition (SLR) is the task of automatically identifying the language present in a speech signal.
Audio Similarity is Unreliable as a Proxy for Audio Quality
Many audio processing tasks require perceptual assessment.
Music Enhancement via Image Translation and Vocoding
Consumer-grade music recordings such as those captured by mobile devices typically contain distortions in the form of background noise, reverb, and microphone-induced EQ.
HEAR: Holistic Evaluation of Audio Representations
The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios.
Neural Pitch-Shifting and Time-Stretching with Controllable LPCNet
Modifying the pitch and timing of an audio signal are fundamental audio editing operations with applications in speech manipulation, audio-visual synchronization, and singing voice editing and synthesis.
Controllable deep melody generation via hierarchical music structure representation
Recent advances in deep learning have expanded possibilities to generate music, but generating a customizable full piece of music with consistent long-term structure remains a challenge.
Context-Aware Prosody Correction for Text-Based Speech Editing
Text-based speech editors expedite the process of editing speech recordings by permitting editing via intuitive cut, copy, and paste operations on a speech transcript.
CDPAM: Contrastive learning for perceptual audio similarity
The DPAM approach of Manocha et al. learns a full-reference metric trained directly on human judgments, and thus correlates well with human perception.
Disentangled Multidimensional Metric Learning for Music Similarity
For this task, it is typically necessary to define a similarity metric to compare one recording to another.
Metric Learning vs Classification for Disentangled Music Representation Learning
For this, we (1) outline past work on the relationship between metric learning and classification, (2) extend this relationship to multi-label data by exploring three different learning approaches and their disentangled versions, and (3) evaluate all models on four tasks (training time, similarity retrieval, auto-tagging, and triplet prediction).
Controllable Neural Prosody Synthesis
Speech synthesis has recently seen significant improvements in fidelity, driven by the advent of neural vocoders and neural prosody generators.
HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks
Real-world audio recordings are often degraded by factors such as noise, reverberation, and equalization distortion.
F0-consistent many-to-many non-parallel voice conversion via conditional autoencoder
Recently, AutoVC, a conditional autoencoders (CAEs) based method achieved state-of-the-art results by disentangling the speaker identity and speech content using information-constraining bottlenecks, and it achieves zero-shot conversion by swapping in a different speaker's identity embedding to synthesize a new voice.
A Differentiable Perceptual Audio Metric Learned from Just Noticeable Differences
Assessment of many audio processing tasks relies on subjective evaluation which is time-consuming and expensive.
Text-based Editing of Talking-head Video
To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material.
Perceptually-motivated Environment-specific Speech Enhancement
This paper introduces a deep learning approach to enhance speech recordings made in a specific environment.
