2. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay Part B Sliding-band Dynamic Range Compression (N. Tiwari & P. C. Pandey, NCC 2014) P. C. Pandey, "Signal processing for improving speech perception by persons with sensorineural hearing loss: Challenges and some solutions", IEEE Workshop on Intelligent Computing, IIIT Allahabad, 13-15 Oct. 2014

3. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 2/25 Overview Introduction Sliding-band Dynamic Range Compression Offline & Real-time Implementations Test Results Summary & Conclusion

4. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 3/25 Introduction Dynamic range compression To present sounds comfortably within the limited dynamic range of the listener by amplifying the low level sounds without making the high level sounds uncomfortably loud. Processing steps Input level estimation Gain calculation based on input level Multiplication of input with gain function Output resynthesis Classification On the basis of signal level calculation: single-band or multiband On the basis of gain control method: feedback or feed-forward

5. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 4/25 Processing Gain dependent on the dynamically varying signal level. Parameters Compression threshold (T H ) Compression ratio (CR) Attack & release time Problems Single-band dynamic range compression Does not account for frequency dependent loudness growth function Power mostly contributed by low-frequency components → amplification of high-frequency components depends low-frequency components → Inaudibility of high frequency components, distortions in temporal envelope

6. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 5/25 Multiband dynamic range compression General scheme of processing Spectral components of the input signal divided in multiple bands and the gain for each band calculated on the basis of signal power in that band. Parameters (band specific): compression threshold T H, compression ratio CR, attack & release time for detection.

7. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 6/25 Lippmann et al. (1980): 16-channel compression 9% improvement in recognition score over linear amplification. Asano et al.(1991): Multiband dynamic range compression realized as a single time-varying FIR filter & implemented on a 32-bit DSP fixed-point processor Less spectral distortion due to smoothened frequency response of FIR filter. Stone et al. (1999): Comparison of single and four-channel compression schemes & effect of varying CR, T H, and attack & release times Intelligibility & quality tests showed no specific preference for schemes. Li et al. (2000): Wavelet-based compression (7 octave sub-band analysis using wavelet filter bank & resynthesis after applying a logarithmic compression on the wavelet coefficients) Increase in intelligibility without introducing noticeable distortions. Magotra et al. (2000): Multiband dynamic range compression using a 16-bit fixed-point processor Taylor's series approximation used for the compression function to reduce computations in gain calculation.

8. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 7/25 Spurious spectral distortions Reduction in spectral contrasts and modulation depth Distortion in spectral shape of formants lying across the band boundaries Distortion of formant transitions across the adjacent bands Time-varying magnitude response without corresponding variation in the phase response leading to quality degradation → Audible distortions, perceptible discontinuities, adverse effect on the perception of certain speech cues Disadvantages of multiband dynamic range compression

9. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 8/25 Example of distortion due to multiband dynamic range compression during spectral transition Processed output: multiband compression with 18 auditory critical bands, CR = 30, T a = 6.4 ms, T r = 192 ms Swept sinusoidal input: constant amplitude, 125 –250 Hz linearly swept frequency, 200 ms sweep duration Time (s)

10. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 9/25 Investigation objective Real-time dynamic range compression to compensate for frequency-dependent loudness recruitment associated with sensorineural hearing loss for use in hearing aids with a low- power processor. Low distortions Low computational complexity & memory requirement Low signal delay (algorithmic + computational)

11. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 10/25 Sliding-band compression Proposed for significantly reducing the temporal and spectral distortions associated with the currently used single-band and multiband compressions in hearing aids. Realized with computational complexity acceptable for implementation on a 16-bit fixed-point DSP processor and signal delay acceptable for real-time application. Investigations using offline & real-time implementations S election of processing parameters Evaluation of the implementations Informal listening, PESQ measure

12. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 11/25 Sliding-band Dynamic Range Compression Processing steps Short-time spectral analysis: windowing, zero-padding, DFT calculation Spectral modification: gain calculation, output spectrum calculation Resynthesis: IDFT calculation, windowing, overlap-add Processing Applying a frequency-dependent gain function, with the gain for each spectral sample determined by the short-time power in auditory critical bandwidth centered at it & in accordance with the specified hearing thresholds, compression ratios, and attack and release times.

13. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 12/25 Spectral modification P mc (k): Power at upper comfortable listening level CR(k): Compression ratio Short-time spectral analysis: windowing (length L, shift S ), zero-padding, N -point DFT Resynthesis: N -point IDFT, overlap-add

14. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 13/25 Auditory critical bandwidth BW(k) = 25 + 75(1 + 1.4f 2 ) 0.69, freq. sample = k, freq. = f Target gain calculation Power at upper comfortable listening level: P mc (k) Compression ratio: CR(k) Input power: P ic (k), Output power: P oc (k) Target gain: G t (k) = P oc (k) / P ic (k) Compression relation dB scale: [P oc (k) / P mc (k)] dB = [P ic (k) / P mc (k)] dB / CR(k) linear scale: P oc (k) / P mc (k) = [P ic (k) / P mc (k)] 1/ CR(k) Target gain for k th spectral sample [G t (k)] dB = [1 − 1 / CR(k)] [P mc (k) / P ic (k)] dB Gain calculation

15. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 14/25 Gain calculation (contd.) Gain changed in steps from the previous value towards the target value with settable attack and release times Fast attack: to avoid the output level from exceeding UCL during transients Slow release: to avoid the pumping effect or amplification of breathing Number of steps during attack phase = s a Number of steps during release phase = s r Target gain corresponding to min. input level = G max Target gain corresponding to max. input level = G min Gain ratio for attack phase γ a = (G max / G min ) 1/sa Gain ratio for release phase γ r = (G max / G min ) 1/sr Gain for i th window & k th spectral sample G(i,k) = max[G(i − 1,k) / γ a, G t (i,k)] for G t (i,k) < G(i − 1,k) min[G(i − 1,k) γ r, G t (i,k)] for G t (i,k) > G(i − 1,k) Attack time T a = s a S / f s, Release time T r = s r S / f s [f s = sampling freq., S = window shift]

16. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 15/25 Analysis-synthesis using least-square error based signal estimation from modified STFT (Griffin & Lim, 1984): Processing artifacts reduced by masking the effect of phase discontinuities in the modified short-time complex spectrum. Look-up table based gain calculation: Two-dimensional look-up table relating the input power with gain as a function of frequency. ◦ Reduces computations for real-time implementation. ◦ Permits compression function most suited to compensate for the abnormal loudness growth. Implementation related challenges Modifications in the short-time magnitude spectrum without corresponding changes in the phase spectrum can cause audible distortions. Computational complexity: log or series approximation based gain calculations not suitable for use in sliding-band compression. Solution

17. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 16/25 Offline & Real-time Implementations Implementation for offline processing Implementation using Matlab 7.10 for evaluating the performance of the proposed technique and the effect of processing parameters. Processing parameters ◦ f s = 10 kHz ◦ Frame length = 25.6 ms ( L = 256 ) ◦ Overlap = 75% ( S = 64 ) ◦ FFT size N = 512 2D look-up table for frequency-dependent compression based on a linear relation between input-dB and output-dB, with settable CR(k) and P mc (k). ◦ Input range: 20 log intervals (trade-off: small gain increments, look-up table size). ◦ Look-up table with 256×20 entries Attack and release times ◦ s a =1, T a = 6.4 ms : Fast attack to avoid uncomfortable level during transients ◦ s r =30, T r = 192 ms : Slow release to avoid pumping & amplification of breathing

18. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 17/25 Implementation for real-time processing Implementation on a 16-bit fixed-point DSP board to examine suitability of the technique for use in hearing aids. DSP chip: TI/TMS320C5515 ◦16 MB memory space ( 320 KB on-chip RAM with 64 KB dual access, 128 KB on- chip ROM) ◦ Three 32 -bit programmable timers ◦4 DMA controllers each with 4 channels ◦ FFT hardware accelerator ( up to 1024 -point FFT) ◦ Max. clock speed: 120 MHz DSP Board: eZdsp ◦ 4 MB on-board NOR flash for user program ◦ Stereo codec TLV320AIC3204: 16/20/24/32-bit ADC & DAC, 8 – 192 kHz sampling Software development: C using TI's 'CCStudio ver. 4. 0

19. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 18/25 Input-output operations: DMA based I/O with cyclic buffers ADC and DAC: one codec (left channel) with 16 -bit quantization Processing parameters (same as for offline processing): f s = 10 kHz, L = 256, S = 64, N = 512 Data representation (input samples, spectral values, processed samples): 16 -bit real & 16 -bit imaginary Implementation

20. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 19/25 Data transfers & buffering operations ( S = L/4 ) DMA cyclic buffers 5 -block S - sample input buffer 2 -block S - sample output buffer Pointers Current input block Just-filled input block Current output block Write-to output block (incremented cyclically on DMA interrupt) Signal delay Algorithmic: 1 frame ( 25.6 ms) Computational ≤ frame shift ( 6.4 ms)

21. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 20/25 Test Results Tests for verification and evaluation Offline processing Verification of the compression technique for speech input with a large level variation and examination of the effect of different set of processing parameters. Assessment of output speech quality (using informal listening) for different input speech materials and time varying levels. Comparison of distortions introduced by different compression techniques during spectral transitions. Real-time processing Comparison of the processed outputs from offline & real-time implementation: informal listening, PESQ measure (0 – 4.5). Signal delay & computational requirement.

22. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 21/25 Example: "you will mark ut please" concatenated with scaling factors for variation in the input level. CR = 2, T a = 6.4 ms, T r = 6.4 & 192 ms. Input waveform Scaling factor Unprocessed waveform Processed T r = 6.4 ms, low P mc Processed T r = 192 ms, low P mc Processed T r = 6.4 ms, high P mc Processed T r = 192 ms, high P mc Time (s) Results from offline processing Processing of different speech materials with varying levels: No audible roughness or distortion during informal listening.

23. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 22/25 Time (s) Distortions during spectral transitions: Example of swept sinusoidal input. Sliding band compression output Multiband compression (18 auditory critical bands) output Single-band compression output Input: constant amplitude, 125 –250 Hz linearly swept frequency, 200 ms sweep duration CR = 30, T a = 6.4 ms, T r = 192 ms.

24. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 12345 23/25 Results from real-time processing Informal listening: real-time output perceptually similar to the offline output PESQ for real-time w.r.t. offline : 3.5 Signal delay = 36 ms Use of processing capacity: 41% (lowest proc. clock for satisfactory operation = 50 MHz, max. clock = 120 MHz) Unprocessed waveform Offline processed waveform Real-time processed waveform Example: "you will mark ut please" concatenated with scaling factors for variation in the input level. CR = 2, T a = 6.4 ms, T r = 192 ms, low P mc. Time (s)

25. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay 1234 5 24/25 Summary & Conclusions Sliding-band dynamic range compression presented to compensate for frequency-dependent loudness recruitment associated with sensorineural hearing loss without introducing the distortions associated with single-band & multiband compression. Realized using modified fixed-frame analysis-synthesis for low computational complexity & without distortions associated with phase discontinuities. Suitable for speech & non-speech audio & provision for settable attack time, release time, & compression ratios. Implemented using 16-bit fixed-point DSP chip & tested for satisfactory operation: 36 ms signal delay, 41% use of processing capacity, indicating scope for combination with other processing techniques.

26. pcpandey@ee.iitb.ac.in EE Dept., IIT Bombay To be continued to Part C.