This paper introduces a sinusoidal recurrence mechanism for harmonic spectral enrichment in implicit neural representations, validated against various models and tasks.

The paper proposes SALMONN-2, an ALLM built on a unified SSL encoder, and presents a multi-layer feature fusion adapter to better exploit hierarchical SSL encoder representations. It also explores mul…

This paper proposes RPPNet, a two-stage deep learning architecture for music generation with variable structural boundaries, which automatically derives grouping of Rhythm-Pitch Primitive sequences fr…

The paper introduces Noise-Aware BEATs (NABEATs), a noise-aware audio self-supervised learning framework that estimates clean BEATs representations from noisy audio signals using an auxiliary referenc…

A deep neural timbre trait predictor is introduced to evaluate neural audio synthesizers' performance using human judgments and correlate with average human ratings.

This paper proposes TF-MossFormer, a time-frequency transformer for monaural speech separation that combines local and global attention using a content-aware sliding-window mechanism.

This paper proposes a neural audio watermarking method that embeds a message into the continuous latent representation of a codec-like speech autoencoder for improved codec robustness.

This paper explains how Rotary Position Embeddings (RoPE) frequencies in transformer models correspond to the relative-distance structure of training data, and the implications for long-context genera…