5 papers
Neural Audio Codec with Adjustable Token Temporal Resolution Using Sampling-Frequency-Independent Convolutional Layers
Tomohiko Nakamura, Wataru Nakata, Kanami Imamura +1
Discrete tokens obtained from neural audio codecs (NACs) have been used as compact representations in audio generation and understanding models. In such token-based systems, token…
Dissecting Performance Degradation in Audio Source Separation under Sampling Frequency Mismatch
Kanami Imamura, Tomohiko Nakamura, Kohei Yatabe +1
Audio processing methods based on deep neural networks are typically trained at a single sampling frequency (SF). To handle untrained SFs, signal resampling is commonly employed, b…
Drum-to-Vocal Percussion Sound Conversion and Its Evaluation Methodology
Rinka Nobukawa, Makito Kitamura, Tomohiko Nakamura +2
This paper defines the novel task of drum-to-vocal percussion (VP) sound conversion. VP imitates percussion instruments through human vocalization and is frequently employed in con…
Multi-Sampling-Frequency Naturalness MOS Prediction Using Self-Supervised Learning Model with Sampling-Frequency-Independent Layer
Go Nishikawa, Wataru Nakata, Yuki Saito +3
We introduce our submission to the AudioMOS Challenge (AMC) 2025 Track 3: mean opinion score (MOS) prediction for speech with multiple sampling frequencies (SFs). Our submitted mod…
Hyperbolic Embeddings for Order-Aware Classification of Audio Effect Chains
Aogu Wada, Tomohiko Nakamura, Hiroshi Saruwatari
Audio effects (AFXs) are essential tools in music production, frequently applied in chains to shape timbre and dynamics. The order of AFXs in a chain plays a crucial role in determ…