1. Adaptive Methods for Speaker Separation in Cars DaimlerChrysler Research and Technology Julien Bourgeois Julien.Bourgeois@daimlerchrysler.com

2. 2 General context x 1 (t)x 4 (t) Microphones Goal: provide individual speech input for each passenger Individual speech flows s 1 (t ) s 2 (t) +Road Noise spatially diffuse Several simultaneous speakers (sources) spatially located Separatio n Algorithm

3. 3 General context x 1 (t)x 4 (t) Microphones Individual speech flows s 1 (t ) s 2 (t) +Road Noise spatially diffuse Several simultaneous speakers (sources) spatially located Separatio n Algorithm Mixing system Goal: provide individual speech input for each passenger

4. 4 General context x 1 (t)x 4 (t) Microphones Individual speech flows s 1 (t ) s 2 (t) +Road Noise spatially diffuse Several simultaneous speakers (sources) spatially located Separatio n Algorithm Software Goal: provide individual speech input for each passenger

5. 5 Plan of the presentation Overview of existing methods Supervised/Informed separation vs. Blind separation Blind separation and prior spatial information Conclusion and future work

6. 6 Existing methods: CASA vs. Multichannel Techniques CASA: 1 microphone separation Heuristics based on an analysis of human auditory system Requires a lot of data (training of parameters) Multi-microphones techniques: Speech moves much faster than… the coherence relating two (or more) microphones.

7. 7 Existing Methods: Beamforming Beamforming: Prior information on target position Constrain the response in the direction of interest Minimize the output power Problem of target cancellation if prior spatial info is not perfect. Filters Direction of interest output

8. 8 Existing methods: Blind Source Separation Blind Source Separation (BSS) First applications to speech separation at the end of the 90’s Only requirement: statistically independent sources Difficult optimization problem: maximizing a nonlinear function (independence measure). With many microphones, target cancellation can also appear. Permutation ambiguity. Acousti c Mixing BSS Sources Dependent Observations Independent Outputs

9. 9 The question is… Is it possible to merge Beamforming and BSS, and combine their advantages? In cars, prior knowlegde on speaker positions, separate blindly is suboptimal.

10. 10 Blind separation and prior spatial information Initialisation of BSS according to speakers positions helps optimisation procedure a lot. Solve permutations problem solved Target cancellation problem solved Prior info : positions Acousti c Mixing BSS Sources Dependent Observations Independent Outputs

11. 11 BSS is not that blind… BSS performances depends dramatically on the type of mixing Strictly causal

12. 12 BSS is not that blind… BSS performances depends dramatically on the type of mixing Strictly causal Non strictly causal

13. 13 Beamforming is not that informed… Perfect prior spatial information is actually not necessary: Target cancellation problem can be solved if one can detect activity/silences of each speaker. The detection problem is strongly related with IDIAP smart meeting room projects. Filters Direction of interest output

14. 14 Conclusion and future works Combining BSS with a beamformer is not gainful. We may inform BSS efficiently in the case of non-causal mixings (algorithmic rotation of the microphone array)

15. 15 Conclusion and future works Combining BSS with a beamformer is not gainful. We may inform BSS efficiently in the case of non-causal mixings (algorithmic rotation of the microphone array)

16. 16 Thank you!