Latest Articles for Audio Processing

A new lightweight model improves pitch estimation using self-supervised learning techniques.

New methods developed to identify fake songs amidst growing concerns.

Learn how technology helps categorize music genres efficiently.

This study explores issues with using convnets for audio filterbank creation.

The CLAP model bridges audio and text processing for various applications.

PIAVE helps machines extract voices clearly, even when speakers turn their heads.

AV2Wav enhances speech quality using audio and visual cues.

Introducing a flexible framework to enhance voice privacy research.

Research reveals emotional speech impacts model performance in speech separation tasks.

New methods are improving our ability to detect fake speech effectively.

New methods enhance vocoder performance with limited audio data.

A robust approach to identify audio anomalies and combat voice spoofing.

Introducing a faster method for high-quality speech synthesis using diffusion models.

HiFTNet offers faster, high-quality speech synthesis using efficient innovative techniques.

AV-SUPERB evaluates audio and visual models across various tasks for better performance.

New method improves speed and efficiency in Text-to-Audio generation.

A new model improves speech separation efficiency and performance.

A new approach generates audio captions using only text, improving data efficiency.

Exploring the challenges and innovations in matching audio recordings to sheet music.

Using k-means clustering to optimize audio data for better model training.

Study shows audio augmentation can enhance speech recognition in low-resource languages.

New strategies enhance weak label learning by selecting relevant negative examples.

A method to choose the best ASR model based on audio features.

Learn how dereverberation boosts speech recognition in noisy environments.

This study presents an attention-based model for estimating room volumes from audio recordings.

ASCA model enhances audio classification accuracy for small datasets.

This study converts MRI tongue data into real speech audio.

This study examines how model compression impacts speech recognition in noisy environments.

Explore how Online Active Learning improves sound recognition efficiency.

A new model improves understanding of speech and sounds simultaneously.

DCLS enhances audio classification performance by learning kernel positions during training.

A new method enhances machine learning of audio-visual data.

A new method enhances sound recognition and source location without labels.

Exploring how sharpness of minima influences model performance on unseen audio data.

A study on using transformers for effective music tagging and representation.

This research presents a model for improving speech clarity across different conditions.

Exploring advancements in automated audio captioning and its impact on accessibility.

New methods enhance linking text descriptions to sound events.

E-SHARC improves speaker identification in various audio environments.

A new approach simplifies audio-visual segmentation without costly labeled data.