Dataset of directional room impulse responses for realistic speech data
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View abstract on PubMed
Summary
This summary is machine-generated.Generating realistic multi-channel speech recordings for moving speakers is challenging. This dataset provides directional Room Impulse Responses (RIRs) from real rooms to enable accurate simulation of reverberant environments.
Area Of Science
- Acoustics
- Signal Processing
- Machine Learning
Background
- Acquiring real-world multi-channel speech data is costly and labor-intensive.
- Existing methods often use simulated Room Impulse Responses (RIRs) from simple 'shoebox' rooms, limiting realism for moving speakers.
- Far-field speech processing for applications like smart assistants requires handling moving speakers in reverberant conditions.
Purpose Of The Study
- To create a dataset of directional RIRs recorded in real environments (classroom, corridor).
- To facilitate the generation of realistic multi-channel speech data for moving speakers.
- To support research in far-field speech processing for home automation and smart assistants.
Main Methods
- Recorded directional RIRs at multiple locations on a fine grid within a classroom and a large corridor.
- Developed a method to simulate moving speakers by generating random trajectories on the RIR grid.
- Utilized the overlap-add method to convolve monaural speech with RIRs for spatialized audio generation.
Main Results
- A comprehensive dataset of directional RIRs from real-world acoustic spaces.
- A validated methodology for simulating moving speakers in reverberant environments.
- Demonstrated the utility of the dataset and methods with an example application.
Conclusions
- The provided dataset significantly enhances the realism of simulated multi-channel speech data.
- Enables more robust development and testing of far-field speech processing systems for dynamic environments.
- Addresses a critical need for realistic data in speech technology research and development.
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