8 papers
Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders
Jonah Casebeer, Vinjai Vale, Umut Isik +3
Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech output…
Communication-Cost Aware Microphone Selection For Neural Speech Enhancement with Ad-hoc Microphone Arrays
Jonah Casebeer, Jamshed Kaikaus, Paris Smaragdis
In this paper, we present a method for jointly-learning a microphone selection mechanism and a speech enhancement network for multi-channel speech enhancement with an ad-hoc microp…
Efficient Trainable Front-Ends for Neural Speech Enhancement
Jonah Casebeer, Umut Isik, Shrikant Venkataramani +1
Many neural speech enhancement and source separation systems operate in the time-frequency domain. Such models often benefit from making their Short-Time Fourier Transform (STFT) f…
Deep Tensor Factorization for Spatially-Aware Scene Decomposition
Jonah Casebeer, Michael Colomb, Paris Smaragdis
We propose a completely unsupervised method to understand audio scenes observed with random microphone arrangements by decomposing the scene into its constituent sources and their…
Multipath-enabled private audio with noise
Anadi Chaman, Yu-Jeh Liu, Jonah Casebeer +1
We address the problem of privately communicating audio messages to multiple listeners in a reverberant room using a set of loudspeakers. We propose two methods based on emitting n…
Multi-View Networks For Multi-Channel Audio Classification
Jonah Casebeer, Zhepei Wang, Paris Smaragdis
In this paper we introduce the idea of multi-view networks for sound classification with multiple sensors. We show how one can build a multi-channel sound recognition model trained…