Search Results for author: Johannes Gehrke

Found 13 papers, 2 papers with code

Programming by Rewards

no code implementations14 Jul 2020 Nagarajan Natarajan, Ajaykrishna Karthikeyan, Prateek Jain, Ivan Radicek, Sriram Rajamani, Sumit Gulwani, Johannes Gehrke

The goal of the synthesizer is to synthesize a "decision function" $f$ which transforms the features to a decision value for the black-box component so as to maximize the expected reward $E[r \circ f (x)]$ for executing decisions $f(x)$ for various values of $x$.

Program Synthesis

The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results

no code implementations16 May 2020 Chandan K. A. Reddy, Vishak Gopal, Ross Cutler, Ebrahim Beyrami, Roger Cheng, Harishchandra Dubey, Sergiy Matusevych, Robert Aichner, Ashkan Aazami, Sebastian Braun, Puneet Rana, Sriram Srinivasan, Johannes Gehrke

In this challenge, we open-sourced a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios consisting of both synthetic and real recordings.

Speech Enhancement

Qd-tree: Learning Data Layouts for Big Data Analytics

no code implementations22 Apr 2020 Zongheng Yang, Badrish Chandramouli, Chi Wang, Johannes Gehrke, Yi-Nan Li, Umar Farooq Minhas, Per-Åke Larson, Donald Kossmann, Rajeev Acharya

For a given workload, however, such techniques are unable to optimize for the important metric of the number of blocks accessed by a query.

The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework

no code implementations23 Jan 2020 Chandan K. A. Reddy, Ebrahim Beyrami, Harishchandra Dubey, Vishak Gopal, Roger Cheng, Ross Cutler, Sergiy Matusevych, Robert Aichner, Ashkan Aazami, Sebastian Braun, Puneet Rana, Sriram Srinivasan, Johannes Gehrke

In this challenge, we open-source a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios consisting of both synthetic and real recordings.

Speech Enhancement Speech Quality

Reinforcement learning for bandwidth estimation and congestion control in real-time communications

no code implementations4 Dec 2019 Joyce Fang, Martin Ellis, Bin Li, Siyao Liu, Yasaman Hosseinkashi, Michael Revow, Albert Sadovnikov, Ziyuan Liu, Peng Cheng, Sachin Ashok, David Zhao, Ross Cutler, Yan Lu, Johannes Gehrke

Bandwidth estimation and congestion control for real-time communications (i. e., audio and video conferencing) remains a difficult problem, despite many years of research.

A scalable noisy speech dataset and online subjective test framework

no code implementations17 Sep 2019 Chandan K. A. Reddy, Ebrahim Beyrami, Jamie Pool, Ross Cutler, Sriram Srinivasan, Johannes Gehrke

Our subjective MOS evaluation is the first large scale evaluation of Speech Enhancement algorithms that we are aware of.

Speech Enhancement

Supervised Classifiers for Audio Impairments with Noisy Labels

no code implementations3 Jul 2019 Chandan K. A. Reddy, Ross Cutler, Johannes Gehrke

The user feedback after the call can act as the ground truth labels for training a supervised classifier on a large audio dataset.

ALEX: An Updatable Adaptive Learned Index

no code implementations21 May 2019 Jialin Ding, Umar Farooq Minhas, JIA YU, Chi Wang, Jaeyoung Do, Yi-Nan Li, Hantian Zhang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David Lomet, Tim Kraska

The original work by Kraska et al. shows that a learned index beats a B+Tree by a factor of up to three in search time and by an order of magnitude in memory footprint.

Sparse Partially Linear Additive Models

1 code implementation17 Jul 2014 Yin Lou, Jacob Bien, Rich Caruana, Johannes Gehrke

Thus, to make a GPLAM a viable approach in situations in which little is known $a~priori$ about the features, one must overcome two primary model selection challenges: deciding which features to include in the model and determining which of these features to treat nonlinearly.

Additive models Model Selection

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