Ambisonics Networks - Regularization Impact on Neural Networks
Ambisonics regularization affects neural network performance.
Detection of Deepfake Environmental Audio: A Study on Fake Sound Detection Using CLAP Embeddings
The study proposes a pipeline for detecting fake environmental sounds using CLAP audio embeddings, achieving high accuracy in identifying deepfake audio.
Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models
Developing realistic digital models of dynamic range compressors using deep learning and state-space models.
Sound Event Detection, Localization, and Distance Estimation Study
Enhancing sound event detection with distance estimation for accurate source localization.
Text-to-Audio Generation Aligned with Videos: T2AV-BENCH
The author introduces T2AV-BENCH, a benchmark for text-to-audio generation aligned with videos, and proposes the T2AV model that integrates visual-aligned text embeddings for improved audio synthesis.
ASiT: Local-Global Audio Spectrogram Vision Transformer for Event Classification
The author proposes ASiT, a novel self-supervised learning framework that combines local and global contextual information through group masked model learning and self-distillation to enhance audio representation and achieve state-of-the-art performance in various audio classification tasks.
RFWave: Multi-band Rectified Flow for Audio Waveform Reconstruction
RFWave introduces a multi-band Rectified Flow approach for high-fidelity audio waveform reconstruction, emphasizing efficiency and quality.
Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities
Audio Flamingo introduces a novel audio language model with strong audio understanding, in-context learning, and multi-turn dialogue abilities. The approach sets new benchmarks in various audio tasks.
Impact of Radial Functions Regularization on Ambisonics Networks
The author investigates the impact of different regularization methods on Deep Neural Network (DNN) training and performance in Ambisonics networks, highlighting the importance of regularization information.
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