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NCC2014 Kanpur, 28 Feb.- 2 Mar. 2014, Paper No. 1569847357 (Session III, Sat., 1st Mar., 1020 1200)

A Sliding-band Dynamic Range Compression for Use in Hearing Aids

Nitya TiwariPrem C. Pandey

{nitya, pcpandey} @ ee.iitb.ac.in

IIT Bombaynitya@ee.iitb.ac.inEE Dept., IIT BombayOverviewIntroductionSliding-band Dynamic Range Compression Offline & Real-time ImplementationsTest ResultsSummary & Conclusion#/27nitya@ee.iitb.ac.inEE Dept., IIT Bombay1. IntroductionSensorineural hearing lossCauses: abnormalities in the cochlear hair cells or the auditory nerveCharacteristicsIncrease in hearing thresholds (due to loss of inner hair cells) Loudness recruitment (abnormal loudness growth) & decrease in dynamic range (due to loss of outer hair cells)Increased spectral & temporal masking, leading to degraded speech perception

Signal processing in hearing aidsFrequency selective amplification to compensate for frequency dependent elevation of hearing thresholdsAmplitude compression to compensate for decreased dynamic range12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayObjectiveTo 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 stepsInput level estimationGain calculation based on input levelMultiplication of input with gain function Output resynthesis

ClassificationOn the basis of signal level calculation: single-band or multibandOn the basis of gain control method: feedback or feed-forwardDynamic range compression12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayProcessingGain dependent on the dynamically varying signal level.ParametersCompression threshold (Th)Compression ratio (CR)Attack & release timeSingle-band dynamic range compressionProblemsDoes not account for frequency dependent loudness growth functionPower mostly contributed by low-frequency components amplification of high-frequency components depends low-frequency components Inaudibility of high frequency components, distortions in temporal envelope

12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayMultiband dynamic range compression

General scheme of processingSpectral 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 Th, compression ratio CR, attack & release time for detection.12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayLippmann et al. (1980): 16-channel compression9% 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 processorLess 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, Th, 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.

12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombaySpurious spectral distortionsReduction in spectral contrasts and modulation depthDistortion in spectral shape of formants lying across the band boundariesDistortion 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 cuesDisadvantages of multiband dynamic range compression12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayExample of distortion due to multiband dynamic range compression during spectral transition

Processed output: multiband compression with 18 auditory critical bands, CR = 30, Ta = 6.4 ms, Tr = 192 ms

Swept sinusoidal input: constant amplitude, 125 250 Hz linearly swept frequency, 200 ms sweep durationTime (s)Time (s)12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayResearch 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 requirementLow signal delay (algorithmic + computational)12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombaySliding-band compressionProposed 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 implementationsSelection of processing parameters

Evaluation of the implementationsInformal listening, PESQ measure 12345#/27nitya@ee.iitb.ac.inEE Dept., IIT Bombay2. Sliding-band Dynamic Range CompressionProcessing steps

Short-time spectral analysis: windowing, zero-padding, DFT calculationSpectral modification: gain calculation, output spectrum calculationResynthesis: IDFT calculation, windowing, overlap-add ProcessingApplying 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.12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombaySpectral modification

Pmc(k): Power at upper comfortable listening levelCR(k): Compression ratio Short-time spectral analysis: windowing (length L, shift S), zero-padding, N-point DFT Resynthesis: N-point IDFT, overlap-add 12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayAuditory critical bandwidth BW(k) = 25 + 75(1 + 1.4f 2)0.69, freq. sample = k, freq. = f

Target gain calculationPower at upper comfortable listening level: Pmc(k)Compression ratio: CR(k)Input power: Pic(k), Output power: Poc(k)Target gain: Gt(k) = Poc(k) / Pic(k)Compression relationdB scale: [Poc(k) / Pmc(k)]dB = [Pic(k) / Pmc(k)]dB / CR(k)linear scale: Poc(k) / Pmc(k) = [Pic(k) / Pmc(k)]1/ CR(k)Target gain for kth spectral sample[Gt(k)]dB = [1 1 / CR(k)] [Pmc(k) / Pic(k)]dB Gain calculation12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayGain calculation (contd.)Gain changed in steps from the previous value towards the target value with settable attack and release timesFast attack: to avoid the output level from exceeding UCL during transients Slow release: to avoid the pumping effect or amplification of breathingNumber of steps during attack phase = sa Number of steps during release phase = srTarget gain corresponding to min. input level = GmaxTarget gain corresponding to max. input level = GminGain ratio for attack phase a = (Gmax / Gmin)1/saGain ratio for release phase r = (Gmax / Gmin)1/sr Gain for ith window & kth spectral sampleG(i,k) = max[G(i 1 ,k) / a, Gt(i,k)] for Gt(i,k) < G(i 1 ,k) min[G(i 1 ,k) r, Gt(i,k)] for Gt(i,k) > G(i 1 ,k)Attack time Ta = saS / fs , Release time Tr = srS / fs [fs = sampling freq., S = window shift]12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayAnalysis-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 challengesModifications 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.Solution12345#/27nitya@ee.iitb.ac.inEE Dept., IIT Bombay3. Offline & Real-time ImplementationsImplementation for offline processing Implementation using Matlab 7.10 for evaluating the performance of the proposed technique and the effect of processing parameters. Processing parameters fs = 10 kHz Frame length = 25.6 ms (L = 256) Overlap = 75% (S = 64) FFT size N = 5122D look-up table for frequency-dependent compression based on a linear relation between input-dB and output-dB, with settable CR(k) and Pmc(k). Input range: 20 log intervals (trade-off: small gain increments, look-up table size). Look-up table with 25620 entriesAttack and release times sa=1, Ta = 6.4 ms: Fast attack to avoid uncomfortable level during transients sr=30, Tr = 192 ms: Slow release to avoid pumping & amplification of breathing12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayImplementation for real-time processingImplementation on a 16-bit fixed-point DSP board to examine suitability of the technique for use in hearing aids.DSP chip: TI/TMS320C551516 MB memory space (320 KB on-chip RAM with 64 KB dual access, 128 KB on-chip ROM)Three 32-bit programmable timers4 DMA controllers each with 4 channelsFFT hardware accelerator (up to 1024-point FFT)Max. clock speed: 120 MHz DSP Board: eZdsp4 MB on-board NOR flash for user programStereo codec TLV320AIC3204: 16/20/24/32-bit ADC & DAC, 8 192 kHz samplingSoftware development: C using TI's 'CCStudio ver. 4.0 12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayInput-output operations: DMA based I/O with cyclic buffersADC and DAC: one codec (left channel) with 16-bit quantizationProcessing parameters (same as for offline processing): fs = 10 kHz, L = 256, S = 64, N = 512Data representation (input samples, spectral values, processed samples): 16-bit real & 16-bit imaginary

Implementation12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayData 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)

12345#/27nitya@ee.iitb.ac.inEE Dept., IIT Bombay4. Test ResultsTests for verification and evaluationOffline processingVerification 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 processingComparison of the processed outputs from offline & real-time implementation: informal listening, PESQ measure (0 4.5).Signal delay & computational requirement.12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayExample: "you will mark ut please" concatenated with scaling factors for variation in the input level. CR = 2, Ta = 6.4 ms, Tr = 6.4 & 192 ms.

Input waveform Scaling factor Unprocessed waveform Processed Tr = 6.4 ms, low Pmc Processed Tr = 192 ms, low Pmc Processed Tr = 6.4 ms, high Pmc Processed Tr = 192 ms, high Pmc

Time (s)

Results from offline processingProcessing of different speech materials with varying levels: No audible roughness or distortion during informal listening.12345#/27nitya@ee.iitb.ac.inEE Dept., IIT Bombay

Time (s)

Distortions during spectral transitions: Example of swept sinusoidal input. Sliding band compression outputMultiband compression (18 auditory critical bands) outputSingle-band compression outputInput: constant amplitude, 125 250 Hz linearly swept frequency, 200 ms sweep durationCR = 30, Ta = 6.4 ms, Tr = 192 ms. 12345#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayResults from real-time processingInformal listening: real-time output perceptually similar to the offline outputPESQ for real-time w.r.t. offline : 3.5Signal delay = 36 msUse of processing capacity: 41% (lowest proc. clock for satisfactory operation = 50 MHz, max. clock = 120 MHz)

Unprocessed waveform Offline processed waveformReal-time processed waveform

Example: "you will mark ut please" concatenated with scaling factors for variation in the input level. CR = 2, Ta = 6.4 ms, Tr = 192 ms, low Pmc.Time (s)12345#/27nitya@ee.iitb.ac.inEE Dept., IIT Bombay5. Summary & ConclusionsSliding-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.Further work Evaluation of speech perception by hearing impaired listeners. Implementation in combination with other techniques (spectral subtraction, multiband frequency compression, etc.) & evaluation.1234 5#/27nitya@ee.iitb.ac.inEE Dept., IIT BombayThank Younitya@ee.iitb.ac.inEE Dept., IIT BombayNational Conference on Communications, 28th Feb. to 2nd Mar., 2014, Kanpur, India (NCC 2014)

A Sliding-band Dynamic Range Compression for Use in Hearing Aids

Nitya Tiwari and Prem C. PandeyDept. of Electrical EngineeringIIT Bombay, Mumbai, IndiaEmail: { nitya, pcpandey } @ ee.iitb.ac.in

Abstract Sensorineural hearing loss is associated with elevated hearing thresholds, reduced dynamic range, and loudness recruitment. Dynamic range compression in the hearing aids is provided for restoring normal loudness of low level sounds without making the high level sounds uncomfortably loud. A sliding-band compression is presented for significantly reducing the temporal and spectral distortions generally associated with the currently used single and multiband compression techniques. It uses a frequency-dependent gain function calculated on the basis of critical bandwidth based short-time power spectrum and the specified hearing thresholds, compression ratios, and attack and release times. It is realized using FFT-based analysis-synthesis and can be integrated with other FFT-based signal processing in hearing aids to save computation. The technique is implemented and tested for satisfactory real-time operation, with sampling frequency of 10 kHz, window length of 25.6 ms with 75% overlap on a 16-bit fixed-point DSP processor with on-chip FFT hardware.nitya@ee.iitb.ac.inEE Dept., IIT BombayReferences[1]H. Levitt, J. M. Pickett, and R. A. Houde, Eds., Senosry Aids for the Hearing Impaired. New York: IEEE Press, 1980.[2]B. C. J. Moore, An Introduction to the Psychology of Hearing, London, UK: Academic, 1997, pp 66107.[3]S. A. Gelfand, Hearing: An Introduction to Psychological and Physiological Acoustics, 3rd ed., New York: Marcel Dekker, 1998, pp. 314318[4]P. N. Kulkarni, P. C. Pandey, and D. S. Jangamashetti, Binaural dichotic presentation to reduce the effects of spectral masking in moderate bilateral sensorineural hearing loss, Int. J. Audiol., vol. 51, no. 4, pp. 334344, 2012.[5] J. Yang, F. Luo, and A. Nehorai, Spectral contrast enhancement: Algorithms and comparisons, Speech Commun., vol. 39, no. 12, pp. 3346, 2003.[6]T. Arai, K. Yasu, and N. Hodoshima, Effective speech processing for various impaired listeners, Proc. 18th Int. Congr. Acoust., Kyoto, Japan, 2004, pp. 13891392.[7]P. N. Kulkarni, P. C. Pandey, and D. S. Jangamashetti, Multiband frequency compression for improving speech perception by listeners with moderate sensorineural hearing loss, Speech Commun., vol. 54, no. 3 pp. 341350, 2012.[8]N. Tiwari, P. C. Pandey, and P. N. Kulkarni, Real-time implementation of multi-band frequency compression for listeners with moderate sensorineural impairment, in Proc. Interspeech 2012, Portland, Oregon, 2012, paper no. 689.[9]P. C. Loizou, Speech Enhancement: Theory and Practice. New York: CRC, 2007.[10]S. K. Waddi, P. C. Pandey, and N. Tiwari, Speech enhancement using spectral subtraction and cascaded-median based noise estimation for hearing impaired listeners, in Proc. Nat. Conf. Commun. 2013, New Delhi, India, doi: 10.1109/NCC.2013. 6487989.[11]H. Dillon, Hearing Aids. New York: Thieme Medical Publisher, 2001.[12]R. E. Sandlin, Textbook of Hearing Aid Amplification, San Diego, Cal.: Singular 2000, pp. 210220.[13]L. D.Braida, N. I. Durlach, R. P. Lippmann, B. L. Hicks, W. M. Rabinowitz, and C. M. Reed, Hearing aidsa review of past research on linear amplification, amplitude compression, and frequency lowering, Journal of the American Speech and Hearing Association Monographs 19, pp. 1114, 1979.nitya@ee.iitb.ac.inEE Dept., IIT Bombay[14]R. P. Lippmann, L . D. Braida, and N. I. Durlach, " Study of multichannel amplitude compression and linear amplification for persons with sensorineural hearing loss," J. Acoust. Soc. Am., vol. 69,no. 2, pp. 524534, 1981.[15]F. Asano, Y. Suzuki, T. Sone, S. Kakehata, M. Satake, K. Ohyama, T. Kobayashi, and T. Takasaka, A digital hearing aid that compensates for sensorineural impaired listeners, in Proc. IEEE ICASSP, pp. 3625 3628, 1991.[16]M. A. Stone, B. C. Moore, J. I. Alcntara, and B. R. Glasberg, "Comparison of different forms of compression using wearable digital hearing aids," J. Acoust. Soc. Am., vol. 106, no. 6, pp. 36033619, 1999.[17]M. Li, H. G. McAllister, N. D. Black, and T. A. Perez, Wavelet based non-linear AGC method for hearing aid loudness compensation, in Proc. IEE Vision, Image and Signal Proc., vol.147, no. 6, pp. 502-507, 2000.[18]E. Zwicker, Subdivision of the audible frequency range into critical bands (Freqenzgruppen), J. Acoust. Soc. Am., vol. 33, no. 2, pp. 248, 1961.[19]J. W. Picone, Signal modeling techniques in speech recognition, in Proc. IEEE, vol. 81, no. 9, pp. 1512 1547, 1993.[20]N. Magotra, S. Kamath, F. Livingston, and M.Ho, Development and fixed-poinot implementation of a multiband dynamic range compression (MDRC) algorithm, in Proc. ACSSC, vol. 1, pp. 428-432, 2000.[21]D. W. Griffin and J. S. Lim, Signal estimation from modified short-time Fourier transform, IEEE Trans. Acoustics, Speech, Signal Proc., vol. 32, no. 2, pp. 236 243, 1984.[22]Texas Instruments, Inc., TMS320C5515 Fixed-Point Digital Signal Processor, 2011, [online] Available: focus.ti.com/lit/ds/symlink/ tms320c5515.pdf.[23]Spectrum Digital, Inc., TMS320C5515 eZdsp USB Stick Technical Reference, 2010, [online] Available: support.spectrumdigital.com/ boards/usbstk5515/reva/files/usbstk5515_TechRef_RevA.pdf[24]Texas Instruments, Inc., TLV320AIC3204 Ultra Low Power Stereo Audio Codec, 2008, [online] Available: focus.ti.com/lit/ds/ symlink/tlv320aic3204.pdf.[25]ITU, Perceptual evaluation of speech quality (PESQ): an objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs, ITU-T Rec., P.862, 2001.[26] N. Tiwari, Dynamic range compression results, 2014, [online] Available: www.ee.iitb.ac.in/~spilab/material/nitya/ncc2014. nitya@ee.iitb.ac.inEE Dept., IIT Bombay10

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