no code implementations • 16 Jan 2024 • Alon Vinnikov, Amir Ivry, Aviv Hurvitz, Igor Abramovski, Sharon Koubi, Ilya Gurvich, Shai Pe`er, Xiong Xiao, Benjamin Martinez Elizalde, Naoyuki Kanda, Xiaofei Wang, Shalev Shaer, Stav Yagev, Yossi Asher, Sunit Sivasankaran, Yifan Gong, Min Tang, Huaming Wang, Eyal Krupka
The challenge focuses on distant speaker diarization and automatic speech recognition (DASR) in far-field meeting scenarios, with single-channel and known-geometry multi-channel tracks, and serves as a launch platform for two new datasets: First, a benchmarking dataset of 315 meetings, averaging 6 minutes each, capturing a broad spectrum of real-world acoustic conditions and conversational dynamics.
2 code implementations • 30 Dec 2021 • Shlomo Kashani, Amir Ivry
The second edition of Deep Learning Interviews is home to hundreds of fully-solved problems, from a wide range of key topics in AI.
no code implementations • 14 Jul 2021 • Amir Ivry, Elad Fisher, Roger Alimi, Idan Mosseri, Kanna Nahir
Here, we present an extended version of that algorithm for multi-superstructure localization, which covers a broader localization area of the user.
no code implementations • 27 Jun 2021 • Roger Alimi, Amir Ivry, Elad Fisher, Eyal Weiss
In addition, high generalization and robustness of the neural network can be concluded, based on the rapid convergence of the corresponding receiver operating characteristic curves.
no code implementations • 25 Jun 2021 • Amir Ivry, Baruch Berdugo, Israel Cohen
A deep neural network, which is trained to separate speech from non-speech frames, is obtained by concatenating the decoder to the encoder, resembling the known Diffusion nets architecture.
no code implementations • 25 Jun 2021 • Amir Ivry, Israel Cohen, Baruch Berdugo
Second, the network is succeeded by a standard adaptive linear filter that constantly tracks the echo path between the loudspeaker output and the microphone.
no code implementations • 25 Jun 2021 • Amir Ivry, Israel Cohen, Baruch Berdugo
To mitigate this mismatch between training data and real data, we simulate an augmented training set that contains nearly five million utterances.
no code implementations • 25 Jun 2021 • Amir Ivry, Israel Cohen, Baruch Berdugo
In this paper, we propose a residual echo suppression method using a UNet neural network that directly maps the outputs of a linear acoustic echo canceler to the desired signal in the spectral domain.