7 citations · 10 across the 13 of their papers we have counts for
14 papers
MLVC: Multi-platform Learned Video Codec for Real-World Deployment
Tanel Pärnamaa, Martin Lumiste, Ardi Loot +3
Neural video codecs have surpassed classical codecs in coding efficiency but remain impractical for deployment due to cross-platform incompatibility and high computational cost. Ex…
Personalized Speech Enhancement Without a Separate Speaker Embedding Model
Tanel Pärnamaa, Ando Saabas
Personalized speech enhancement (PSE) models can improve the audio quality of teleconferencing systems by adapting to the characteristics of a speaker's voice. However, most existi…
The ICASSP 2024 Audio Deep Packet Loss Concealment Challenge
Lorenz Diener, Solomiya Branets, Ando Saabas +1
Audio packet loss concealment is the hiding of gaps in VoIP audio streams caused by network packet loss. With the ICASSP 2024 Audio Deep Packet Loss Concealment Grand Challenge, we…
ICASSP 2024 Speech Signal Improvement Challenge
Nicolae Catalin Ristea, Ando Saabas, Ross Cutler +3
The ICASSP 2024 Speech Signal Improvement Grand Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems. This marks…
ICASSP 2023 Acoustic Echo Cancellation Challenge
Ross Cutler, Ando Saabas, Tanel Parnamaa +7
The ICASSP 2023 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and is…
DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic Echo Cancellation, Noise Suppression and Dereverberation
Evgenii Indenbom, Nicolae-Catalin Ristea, Ando Saabas +3
Acoustic echo cancellation (AEC), noise suppression (NS) and dereverberation (DR) are an integral part of modern full-duplex communication systems. As the demand for teleconferenci…
PLCMOS -- a data-driven non-intrusive metric for the evaluation of packet loss concealment algorithms
Lorenz Diener, Marju Purin, Sten Sootla +3
Speech quality assessment is a problem for every researcher working on models that produce or process speech. Human subjective ratings, the gold standard in speech quality assessme…
ICASSP 2023 Speech Signal Improvement Challenge
Ross Cutler, Ando Saabas, Babak Naderi +3
The ICASSP 2023 Speech Signal Improvement Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems. The speech signal…
Deep model with built-in cross-attention alignment for acoustic echo cancellation
Evgenii Indenbom, Nicolae-Cătălin Ristea, Ando Saabas +2
With recent research advances, deep learning models have become an attractive choice for acoustic echo cancellation (AEC) in real-time teleconferencing applications. Since acoustic…
INTERSPEECH 2022 Audio Deep Packet Loss Concealment Challenge
Lorenz Diener, Sten Sootla, Solomiya Branets +3
Audio Packet Loss Concealment (PLC) is the hiding of gaps in audio streams caused by data transmission failures in packet switched networks. This is a common problem, and of increa…
ICASSP 2022 Acoustic Echo Cancellation Challenge
Ross Cutler, Ando Saabas, Tanel Parnamaa +5
The ICASSP 2022 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and sti…
AECMOS: A speech quality assessment metric for echo impairment
Marju Purin, Sten Sootla, Mateja Sponza +2
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…
ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets, Testing Framework, and Results
Kusha Sridhar, Ross Cutler, Ando Saabas +6
The ICASSP 2021 Acoustic Echo Cancellation Challenge is intended to stimulate research in the area of acoustic echo cancellation (AEC), which is an important part of speech enhance…
Crowdsourcing approach for subjective evaluation of echo impairment
Ross Cutler, Babak Naderi, Markus Loide +2
The quality of acoustic echo cancellers (AECs) in real-time communication systems is typically evaluated using objective metrics like ERLE and PESQ, and less commonly with lab-base…