Delay-and-Sum Beamforming-Based Spatial Mapping for Multisource Sound Localization
Bibliographic record
Abstract
Multi-source sound localization can find applications in many domains including auditory scene analysis, fault detection and diagnosis in manufacturing, augmented reality, etc. In far fields, 3D sound source localization is equivalent to finding the direction of arrival (DOA), namely, the azimuth and elevation angles of sound sources. Recent DOA estimation pipelines take multichannel audio inputs, extract spectral features from each channel and then feed them into a deep neural network. Unfortunately, the spectral features contain only the time-frequency information of the audio signals, while spatial information is only implicitly captured in the signals across different channels, which is highly dependent on the acoustic array geometry. To embed the spatial information of the sound source into the spectral feature representation, we propose a DSB-based spatial mapping method encode sound source location information. It can be combined with different feature extraction methods and machine learning models for DOA estimation. Furthermore, a redundancy removal procedure is proposed to accelerate DSB computation so that the pipeline can run in real-time on embedded GPUs, such as NVidia Jeston Nano. We conduct extensive experiments using two neural network models along with the DSB method on two datasets. The experiments demonstrate that the DOA errors can be effectively reduced using the DSB method. When combining DSB for feature extraction, the DOA errors are reduced by up to 19.24%. In addition, the feature extraction process is accelerated by up to 30.42% after the application of redundancy removal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".