A Simple Two-Microphone Array Devoted to Speech Enhancement and Source Tracking
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1 A Simple Two-Microphone Array Devoted to Speech Enhancement and Source Tracking A. Álvarez, P. Gómez, R. Martínez and, V. Nieto Departamento de Arquitectura y Tecnología de Sistemas Informáticos Universidad Politécnica de Madrid Facultad de Informática Campus de Montegancedo, s/n, Boadilla del Monte, Madrid, SPAIN Abstract: - Beamformers are well known for their high angular selectivity, based in directional source separation processes, which makes them potentially suitable for speech enhancement applications in noisy backgrounds. Also, they achieve an important advantage: the lower number of signal sensors (microphones) required. The scheme that is proposed in this paper combines Beamforming and Spectral Subtraction (a well-known method for removing noise from noise-corrupted speech signals) in order to obtain large gains in the SN ratios at a reasonable low computational cost. This method may be used to eliminate or enhance a specific signal using a simple binaural array. The fundamentals of the technique are reviewed and some results obtained in a real environment are also included. Applications of this technique may be found in improving Speech Recognition Methods for Surveillance Systems or Domotic Control. Key-Words: - Binaural negative array processing, Speech enhancement, Source tracking, Noise cancellation. 1 Introduction Nowadays, speech recognition is considered one of the key technologies for producing really useful enduser interfaces. However, speech recognition techniques suffer from a lack of robustness. That is especially relevant when, as it happens in many situations (e.g. Fig. 1), several speakers or speech/sounds sources are active at the same time. Besides, in many situations, it is desirable for the speakers not to wear or use proximity microphones. Therefore, the key to allow a flexible use of speech in cocktail party scenarios is Array Beamforming [4]. Through this paper, the use of Beamformers, as the one shown in Fig. 2, is proposed. These structures achieve two main advantages: narrow negative beams and a lower number of processing elements required, as compared with classical beamformers [6][10][11]. Fig. 1. Example of application in which the lack of robustness reduces severely the applicability of speech-controlled end-user interfaces.
2 The output signal produced by the negative beamformer is then used to feed the other main system module, which comprises a Nonlinear Spectral Subtractor [2]. 2 beamforming The Beamformer (NBF) module, introduced in Fig. 2, recalls the behavior of a notch filter controlled by the β parameter. This mechanism shows a transfer function in the frequency domain, which may be formulated as [7]: Y j( π δ ) 2 ( α, δ ) = 2e (( 1 2β ) cosα sinδ 2 sinα cosδ 2) (1) where: x(tζ) D x(t) D x(t-ζ) x 1 x 2 T T 2πfD α = ωζ = 2 πf = 2πfζ = sin (2) c f δ = ωt = 2 πft = 2πfkτ = 2πk (3) being the angle of arrival, ζ half the array travel time, f the frequency of the signal, k the delay order, 2D the microphone distance and f s the sampling frequency. An example containing the evaluation of formula (1) when plotted against f and may be seen in Fig. 3. f s 1β Fig. 2. Elementary cell of the two-microphone negative beamformer. Microphones are separated a distance d=2d, being the angle of arrival for an incoming sound source. The angular tracking factor is modeled through parameter β. This processing element introduces a delay interval T=kτ, being τ the time delay unit. β y A critical issue is related to the adaptation parameter β. This coefficient in the range [-1.0, 1.0] has to be adjusted in order to detect different arriving source. However, the value of β which produces the highest degree of cancellation, not only depends on the value of the arriving angle, but on the signal frequency f, as well. This property is clearly shown in equation (4) and in Fig. 4. 2πfD tan sin n c β = tan( δ 2) (4) The solution to that problem when managing with broad-band signals (e.g. speech), is to divide into different bands the spectrum of study. The last may be achieved by the use of bandpass filters (see Table 1), and the subsequent replicating the of beamformer cell previously presented. The complete structure implementing this strategy is shown in Fig. 5. The signal from each microphone is first band-limited using one of the 20 bandpass filters. Then, every two corresponding channels feed the bank of Beamformers. A Linear Combiner produces the total output signal, whether canceling or enhancing a given source, as desired [5] º 28º 23º 18º 13º 8º 3º Angle -2º -7º -12º -17º -22º -27º -32º -37º 300Hz 0Hz 1200Hz 900Hz 600Hz 2400Hz 2100Hz 1800Hz 1500Hz 3600Hz 3300Hz 3000Hz 2700Hz 4800Hz 4500Hz 4200Hz 3900Hz 5400Hz 5100Hz Fig. 3. Module of the negative beamformer transfer function for d=5 cm, f s =11,025 Hz, β=0.25 and k=1.
3 Xa Xb Beta Angle 36.5º 32.5º 28.5º 24.5º 20.5º 16.5º 12.5º 8.5º 4.5º 0.5º -3.5º -7.5º -11.5º -15.5º -19.5º -23.5º -27.5º -31.5º -35.5º Fig. 4. Different frequency values mapping for combinations of angle of arrival vs. β. The 20 frequency values correspond to the center of the bands defined in Table 1. Band filter #1 Band filter #2 Band filter #20 Band filter #1 Band filter #2 Beamformer #1 Beamformer #2 Beamformer #20 Yab_2 Yab_1 Yab_20 Linear Combiner 150 Hz 250 Hz 350 Hz 450 Hz 550 Hz 650 Hz 750 Hz 850 Hz 950 Hz 1050 Hz 1150 Hz 1300 Hz 1150 Hz 1550 Hz 1800 Hz 2200 Hz 2500 Hz 3000 Hz 3500 Hz 4300 Hz Zab arrival for a source present in the microphone inputs independently of the presence of other sources. This analysis may done thorough estimations of the power of the beamformer output signal y(t) [1][3]. The solution proposed in the system described in this paper, is to track the presence of the different sources in the frequency domain [8] introducing a measure called Groove Aspect Factor to determine if there are one or more sources active at the corresponding band. Once individual sources have been located, a Band Cross-mapping will infer their presence in other bands, and help in estimating their angle of arrival and its particular Steering Factor [6]. 3 Spectral subtraction To implement the filtering in the spectral domain [2](Fig. 6), the negative beamformer output z a is used as the primary signal, and one of the microphone inputs x ab is used as the reference one. z a x ab FFT a FFT a Z n(m) a X n(m) a g n(m) = X n(m) a / Z n(m) a Band filter #20 β-tracking Module Fig. 5. Algorithmic structure to track a specific sound source. s ab IFFT g' n(m)= Log ( g n(m) ) Band # Range Band # Range Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Hz Table 1. Bank of filters applied to the input signals x a and x b (see Fig. 5). The other important aspect related to the use of the negative beamformer is the individual source tracking, which involves finding the correct angle of Fig. 6. Spectral Subtraction method being used for signal recovery purposes. Time and frequency indices are given by n and m. The calculation of the resulting subtracted signal comprises several stages. In a first step, both signals are segmented in overlapped windows and transformed into the frequency domain. Next, the relationship between the power spectra of the negative beamformer output and the microphone input is calculated for every frequency channel. After that, the ratio is weighted using a logarithmic law before subtracting. Finally, subtraction result is combined with a spectral flooring technique to limit the presence of artificial tones. The phase of the enhanced signal is recovered from the microphone input trace.
4 4 Results The framework of the experiments carried out, is shown in Fig. 7. The left source s 1 (t) corresponds to the signal represented in Fig. 8 (Spanish word /abajo/). The right source s 2 (t) corresponds to a double production of the word /down/ uttered by a different speaker. The power of the signal s 2 (t) is slightly lower than the power of s 1 (t). The two sources are captured by microphones m 1 and m 2, separated 5 cm. The separation between s 1 (t) and m 1 is 40 cm. and the distance between s 2 (t) and m 2 is 60 cm. Finally, the arriving angles 1 and 2 are equal to 22.5º. exactly, Fig. 10.a contains the restoration of the signal presented in Fig. 8 when intermingled with the other speech source s 2 (t). y s 1 (x 1,y 1 ) s 2 (x 2,y 2 ) 2 1 m 2 m 1 2D x Fig. 9. Power spectrum of the Beamformer output when the Band Steering Factors (β j ) are tuned to an incoming angle of 22.5º and -22.5º respectively. Fig. 7. Two signal sources (loudspeakers) s 1 (t) and s 2 (t) are placed on the same plane relative to an array of microphones (m 1 and m 2 ). Fig. 8. Power spectrum of an utterance of the Spanish word /abajo/ (down). Fig. 10 shows the result of the tracking process of signals s 1 (t) and s 2 (t) (see Fig. 9), and further cancellation of s 2 (t) and s 1 (t), respectively. More Fig. 10. Power spectrum coming out from the output of the Spectral Subtraction Module. The two resulting signals show the enhancement of one of the signal in the pair and the cancellation of the other one.
5 5 Conclusions The combination of the herein-proposed methods ( Beamformer Filtering and - Domain Spectral Subtraction) results in a high degree of source enhancement or cancellation at a reasonable computational cost that allows execution in real time. These structures are rather selective in the angular domain attaining signal enhancement factors up to 20 db. Depending of the computational power available, the technique may be easily extended to follow different sources, once the best steering factors are determined for each source. Among other important application fields, we may emphasize clean speech monitoring, and noise removal in Robust Speech Recognition Systems and source localization in Combined Audio-Video Applications [9]. 6 Acknowledgments This research is being supported by Project TIC (Programa Nacional de las Tecnologías de la Información y las Comunicaciones), Project 07T/0001/2000 (Plan Regional de Investigación de la Comunidad Autónoma de Madrid), and a Contract between the Centre Suisse d'electronique et de Microtechnique and Universidad Politécnica de Madrid. 7 References [1] Affes, S., and Grenier, Y., A signal sub-space tracking algorithm for microphone array processing of speech, IEEE Trans. on Speech and Audio Proc., Vol. 5, No. 5, September 1997, pp [2] Álvarez, A, Gómez, P., Martínez, R.. and, Nieto V., Combination of Beamforming and Nonlinear Spectral Subtraction for Speech Enhancement and Source Tracking, 2001 IEEE- EURASIP Workshop on Nonlinear Signal and Image Processing, Baltimore, Maryland, USA, June 3-6, 2001 (to be published). [3] Bodden, M., and Blauert, J., Separation of Concurrent Speech Signals: A Cocktail-Party- Processor for Speech Enhancement, Proc. of the ESCA-Workshop on Speech Processing in Adverse Conditions, Cannes, France, November, 1992, pp [4] Fisher, S., and K. U. Simmer, Beamforming Microphone Arrays for Speech Acquisition in Noisy Environments, Speech Communication, Vol. 20, 1996, pp [5] Gómez, P., Álvarez, A, Martínez, R, Nieto, V., and Rodellar, V., Speech Enhancement through Binaural Filtering, Proc. of X European Signal Processing Conference, September 4-8, 2000, Tampere, Finland [6] Gómez, P., Alvarez, A., Martínez, R., Nieto V. and, Rodellar, V., -Domain Steering for Beamformers in Speech Enhancement and Directional Source Separation, Proc. of ISCAS'2001, Sydney, Australia, May 6-9, 2001 (to be published). [7] Gómez, P., Rodellar, V., Alvarez, A., Martínez, R., Nieto, V., Sacristán, M. A., Newcomb, R. W., Robust Speech Processing based on Binaural Hearing Systems, Proc. of the Midwest Symposium on Circuits and Systems MWSCAS'99, New Mexico, August 8-11, [8] Schmidt, G., Acoustic Echo Control in Subbands an Application of Multirate Systems, Proc. of the EUSIPCO 98, Rhodos, Greece, September 8-11, 1998, pp [9] Strobel, N., Spors, S., and Rabenstein, R. Joint Audio-Video Object Localization and Tracking. A Presentation of General Metholodogy, IEEE Signal Processing Magazine, January 2001, pp [10] Van Compernolle, D. and Van Gerven, S. Beamforming with Microphone Arrays, Applications of Digital Signal Processing to Telecommunications, pp , E.U COST 229. [11] Van Gerven, S. Adaptive Noise Cancellation and Signal Separation with Applications to Speech Enhancement, PhD thesis, K.U.Leuven, ESAT, March, 1996.
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