- Transducers
- Spatial and Multichannel Audio
- Source Separation and Signal Enhancement
- Room Acoustics and Acoustic System Modeling
- Network Audio
- Audio for Multimedia
- Audio Processing Systems
- Audio Coding
- Audio Analysis and Synthesis
- Active Noise Control
- Auditory Modeling and Hearing Aids
- Bioacoustics and Medical Acoustics
- Music Signal Processing
- Loudspeaker and Microphone Array Signal Processing
- Echo Cancellation
- Content-Based Audio Processing
- Read more about AECMOS: A speech quality assessment metric for echo impairment
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Traditionally, the quality of acoustic echo cancellers is evaluated using intrusive speech quality assessment measures such as ERLE \cite{g168} and PESQ \cite{p862}, or by carrying out subjective laboratory tests. Unfortunately, the former are not well correlated with human subjective measures, while the latter are time and resource consuming to carry out. We provide a new tool for speech quality assessment for echo impairment which can be used to evaluate the performance of acoustic echo cancellers.
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- Read more about ICASSP 2022 DEEP NOISE SUPPRESSION CHALLENGE
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- Read more about ICASSP 2022 DEEP NOISE SUPPRESSION CHALLENGE
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- Read more about Modeling Beats And Downbeats With A Time-frequency Transformer
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- Read more about A ROBUST DEEP AUDIO SPLICING DETECTION METHOD VIA SINGULARITY DETECTION FEATURE
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There are many methods for detecting forged audio produced by conversion and synthesis. However, as a simpler method of forgery, splicing has not attracted widespread attention.
Based on the characteristic that the tampering operation will cause singularities at high-frequency components, we propose a high-frequency singularity detection feature obtained
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- Read more about A ROBUST DEEP AUDIO SPLICING DETECTION METHOD VIA SINGULARITY DETECTION FEATURE
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There are many methods for detecting forged audio produced by conversion and synthesis. However, as a simpler method of forgery, splicing has not attracted widespread attention.
Based on the characteristic that the tampering operation will cause singularities at high-frequency components, we propose a high-frequency singularity detection feature obtained
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- Read more about INTERPRETING INTERMEDIATE CONVOLUTIONAL LAYERS IN UNSUPERVISED ACOUSTIC WORD CLASSIFICATION
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Understanding how deep convolutional neural networks classify data has been subject to extensive research. This paper proposes a technique to visualize and interpret intermediate layers of unsupervised deep convolutional networks by averaging over individual feature maps in each convolutional layer and inferring underlying distributions of words with non-linear regression techniques. A GAN-based architecture (ciwGAN [1]) that includes a Generator, a Discriminator, and a classifier was trained on unlabeled sliced lexical items from TIMIT.
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- Read more about Don’t Separate, Learn to Remix: End-to-End Neural Remixing with Joint Optimization
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- Read more about Upmixing via Style Transfer: A Variational Autoencoder for Disentangling Spatial Images and Musical Content
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- Read more about FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR
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We present a neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment. Our FAST-RIR takes rectangular room dimensions, listener and speaker positions, and reverberation time as inputs and generates specular and diffuse reflections for a given acoustic environment. Our FAST-RIR is capable of generating RIRs for a given input reverberation time with an average error of 0.02s.
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