Projects from Prof. Dr. Reinhold Häb-Umbach

Communications Engineering / Heinz Nixdorf Institute

28 projects were found

WestAI - AI Service Center West

Förderprogramm KI7_Aufbau von KI-Servicezentren

Duration: 11/2022 - 12/2025

Funded by: BMBF

Contact: Dr.-Ing. Jörg Schmalenströer

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SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems

Current systems that incorporate AI technology mainly target the introduction phase, where a core component is training and adaptation of AI models based on given example data. SAIL’s focus on the full life-cycle moves the current emphasis towards sustainable long-term development in real life. The joint project SAIL addresses both basic research ...

Duration: 08/2022 - 07/2026

Funded by: MKW NRW

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TRR 318 - Constructing Explainability

In unserer digitalen Gesellschaft nehmen die algorithmischen Ansätze (wie das maschinelle Lernen) rasant an Komplexität zu. Diese erschwert es den Bürger:innen, die Assistenz nachzuvollziehen und die von Algorithmen vorgeschlagenen Entscheidungen zu akzeptieren. Als Antwort auf diese gesellschaftliche Herausforderung hat die Forschung begonnen, ...

Duration: 07/2021 - 06/2025

Funded by: DFG

TRR 318 - Technically enabled explanation of speaker traits (Subproject C06)

A voice might be described as hoarse, or it may be clear, deep, or breathy. Researchers in Project C06 are looking at issues of how different vocal traits sound, and how a voice can be represented in all of its many facets. Here, linguists and computer scientists are working to develop an intelligent system that professionals can use to explain the ...

Duration: 07/2021 - 06/2025

Funded by: DFG

Automatic transcription of conversation situations

Multi-talker conversational speech recognition is concerned with the transcription of audio re­cordings of formal meetings or informal get-to­gethers in machine-readable form using distant microphones. Current solutions are far from reaching human performance. The difficulty of the task can be attributed to three factors. First, the recording ...

Duration: 05/2021 - 12/2024

Funded by: DFG

Learning deep speech representations for phonetics research

The speech signal is a rich source of informa­tion that conveys not only linguistic but also extra/para-linguistic information, such as the speaker's identity, gender, emotional state, age, or the social status. However, those traits are hidden in complex, non-transparent varia­tions of the speech signal, and mostly obscure to speech research. With ...

Duration: 04/2021 - 12/2024

Funded by: DFG

Explainable Feature Importance: Interpretable machine learning through game theoretic analysis of influence variables and interaction effects

Machine learning (ML) methods support the search for patterns in data and relationships between variables, e.g. in complex bio-medical systems. In this way, they can provide new insights and improve decisions in fields of action such as medical diagnostics. In addition to the quality of the models learned from the data, the trust of human experts ...

Duration: 01/2021 - 12/2024

Funded by: MKW NRW

Technically Enabled Explaining of Speaker Traits

The speech signal is a rich source of informa­tion that conveys linguistic but also what is termed para- or extralinguistic content, revea­ling a speaker’s identity, gender, emotional or cognitive state, age, and health. These traits have been the subject of many investigations in phonetics, but due to the high complexity of the underlying ...

Duration: 01/2021 - 12/2025

Funded by: DFG

Acoustic Sensor Networks Research Group

This Research Unit investigates solutions and limitations for acoustic signal processing and classification over coustic sensor networks. We aim to tackle current shortcomings and to develop a common platform which will make ASNs more adaptive to the variability of acoustic environments and sensor configurati­ons, less dependent on supervision, and ...

Duration: 01/2017 - 12/2023

Funded by: DFG

Sound Recognition with Limited Supervision over Sensor Networks

A mismatch between training and test data statistics can result in a significant degradation of performance of machine learning systems. For sound recognition in acoustic sensor net­works (ASNs) this is a significant issue because of the huge number and variability of sounds and acoustic environments, and because of the large variety of sensor ...

Duration: 01/2017 - 12/2023

Funded by: DFG