Digital Speech Processing and Coding

Transcription

1 ENEE408G Spring 2006 Lecture-2 Digital Speech Processing and Coding Spring 06 Instructor: Shihab Shamma Electrical & Computer Engineering University of Maryland, College Park ENEE408G Capstone -- Multimedia Signal Processing (S'03)

2 Last Lecture UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) Course overview and logistics Bring multimedia to digital world: sampling & quantization Introduction to speech processing Different aspects of speech Friday Lab Session Warm-up with Matlab and PocketPC Today: speech processing and coding ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [2]

3 UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) Speech Production ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [3]

4 UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) Visualize Speech via Spectrogram Short-time Fourier Transform (STFT) of the windowed speech waveform is X ( ω, τ ) = x[ n, τ ]exp[ jω n] A window function is applied to the speech signal Spectrogram is Visualized using different brightness in a 2-D time-frequency plot S( ω, τ) = X( ω, τ) 2 x [ n, τ ] = w[ n, τ ] x[ n] ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [4]

5 Example of Speech Spectrogram Figure 3 of SPM May 98 Speech Survey ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [5]

6 Play Speech Figure 1 of SPM May 98 Speech Survey UMCP ENEE408G Slides (created by M.Wu 2003) First speaking machine in 1930s NY World s Fair 14 keys, 1 wristband, 1 pedal Modeling speech production as a linear system Sound sources Either voiced or unvoiced Voice sound Modeled by a generator of pulses Unvoiced sound Modeled by white noise generator Articulation Modeled by a cascade of singleresonance (pole) digital filters ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [6]

7 UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) Linear Separable Model for Speech Production Vocal tract is modeled as a linear time-varying system Parameters of the linear system are slowly varying Excited by time-varying source (voiced or unvoiced) Practical models Model each speech frame as Linear Time-Invariant Excited by either voiced or unvoiced source Allow overlaps in neighbouring frames Figure 3.2 of Furui s book ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [7]

8 Pitch UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) Pitch period: time duration of one glottal cycle (of glottis) Pitch: reciprocal of pitch period Also known as the fundamental frequency Vowel typically has one to four pitch periods Pitch ranges from about 60Hz to 400 Hz Typical pitch range of female pitch is higher than male pitch ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [8]

9 Pitch of Male and Female Speakers UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) Distribution, mean, and standard deviation of male and female pitch Figure 2.12 & 2.11 of Furui s book ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [9]

10 UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) Formant Figure 2.5 & 3.5 of Furui s book Formants: the resonance frequencies of the vocal tract Vowels typically have three formants: F1, F2, F3 Formant changes when vocal tract changes to produce different sound ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [10]

11 Formants of Vowels Figure 2.6 & 2.7 of Furui s book UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [11]

12 Speech Coding ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [12]

13 PCM coding UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) I(x,y) Sampler Quantizer Encoder Input signal digitize/capture device How to encode a digital image into bits? Sampling and perform uniform quantization Pulse Coded Modulation (PCM) 8 bits per sample ~ good for speech 16 bits ~ needed for high-quality music Tradeoff between fidelity and file size How to squeeze out redundancy? transmit ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [13]

14 Discussion on Improving PCM (1) UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) Uniform quantization may give inconsistent range of relative amount errors E.g., +/- 2 incurs 20% vs. 2% at amplitude 10 and 100 Also is not optimal to non-uniformly distributed sources Non-uniform quantization Assign smaller quantization step size at small amplitude to maintain consistent range of relative quantization errors over the entire dynamic range Can apply non-linear transform before uniform quantization via companding (compression-expansion) μ-law companding: international standard for 64kbps speech ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [14]

15 Discussion on Improving PCM (2) UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) Quantized PCM values may not be equally likely Can we do better than encode each value using same # bits? Example P( 0 ) = 0.5, P( 1 ) = 0.25, P( 2 ) = 0.125, P( 3 ) = If use same # bits for all values Need 2 bits to represent the four possibilities if treat If use less bits for likely values 0 ~ Variable Length Codes (VLC) 0 => [0], 1 => [10], 2 => [110], 3 => [111] Use 1.75 bits on average ~ saves 0.25 bit per sample! Bring probability into the picture Use probability distribution to reduce average # bits per quantized sample ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [15]

16 UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) Entropy coding Idea: use less bits for commonly seen values Examples: Huffman coding (used in JPEG and MPEG) Lemple-Ziv coding (used in Unix) How many # bits needed? Compressability depends on the source s characteristics Limit of compression => Entropy Measures the uncertainty or amount of avg. information of a source ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [16]

17 List of Compression Tools UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) Lossless encoding tools Entropy coding: Huffman, Lemple-Ziv, and others (Arithmetic coding) [ Run-length coding ] Lossy tools for reducing redundancy Quantization and truncations Predictive coding Encode prediction parameters and residues with less bits Subband and Transform coding Encode different frequency bands Transform into a domain with improved energy compaction ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [17]

18 UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) How to Encode Correlated Sequence? Consider: high correlation between successive samples Predictive coding Basic principle: Remove redundancy between successive pixels and only encode residual between actual and predicted Residue usually has much smaller dynamic range Allow fewer quantization levels for the same MSE => get compression Compression efficiency depends on intersample redundancy First try e Q (n) u(n) Predictor _ e(n) Quantizer u P (n) = u(n-1) u Q (n) e Q (n) Encoder + u P (n) = u Q (n-1) Predictor Decoder ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [18]

19 UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) Predictive Coding (cont d) Problem with 1 st try Input to predictor are different at encoder and decoder decoder doesn t know u(n)! Mismatch error could propagate to future reconstructed samples Solution: Differential PCM (DPCM) Use quantized sequence u Q (n) for prediction at both encoder and decoder Prediction error e(n) Quantized prediction error e Q (n) Distortion d(n) = e(n) e Q (n) u(n) e Q (n) + _ + u P (n) = u Q (n-1) e(n) u P (n) =u Q (n-1) Encoder Quantizer u Q (n) Predictor Predictor Decoder Think: what predictor to use? e Q (n) u Q (n) ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [19]

20 All-Pole Modeling of Speech UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) Auto-regressive (AR) model: all-pole filter u[n] H(z) is the overall transfer function Glottal Flow G(z), Vocal Tract V(z), Radiation R(z), Gain β Synthesis process: u[n]: the vocal tract input, s[n]: speech output u[n] β H ( z) = s[n] A( z) β H ( z) = s[n] A( z) ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [20]

21 Model-based Coding UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) Linear Prediction Coder (LPC) LPC Vocoder ( voice coder ) Divide speech into frames (several tens milliseconds) and encode the LPC coefficients of each frame Additional parameters to facilitate synthesis: voiced/unvoiced flag, gain, pitch (for voiced) Line Spectrum Pair (LSP) Coding Hybrid Coding: LPC Residual Coding Between LPC and waveform coding ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [21]

22 Line Spectrum Pair (LSP) Coding UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) Pros and Cons of LPC method Good performance at coding rate down to 2.4kbps Synthesized voice becomes unnatural when below 2.4kbps When the poles are near the unit circle, quantization in LPC coefficients may result in instability. LSP parameters LSP are frequencies extracted from polynomials constructed from LPC coefficients Frequency domain features (similar to formant) => produce less distortion due to quantization [See details in Design Project on Speech] ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [22]

23 Hybrid Coding UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) Hybrid between LPC and waveform coding LPC Residual Coding: encode and slowly update LPC coefficients, and send the LPC residual (e.g. encoded using Vector Quantization) Advantages: Free from quality degradation due to source modeling Low-frequency waveform is exactly reproduced Spectral information of the entire frequency range is preserved No need of pitch period estimation and voiced/unvoiced decision ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [23]

24 Code-Excited Linear Predictive Coding (CELP) UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) Multipulse-Excised Linear Predictive Coding (MPC) Do not distinguish voiced/unvoiced sound explicitly Code-Excited Linear Predictive Coding (CELP) Replace the multi-pulses of MPC with vector-quantized sequences based on long-term prediction of periodicity and short-term prediction Figure 6.32 of Furui s book ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [24]

25 Speech Coding Methods Table 6.1 of Furui s book UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) Waveform coding; Hybrid coding; Analysis-synthesis coding ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [25]

26 Speech Quality vs. Transmission Rate UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) Figure 6.2 of Furui s book ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [26]

27 Comparison of Different Speech Coding Tech. UMCP ENEE408G Slides (created by R. Liu & M.Wu 2002) Table 6.2 of Furui s book ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [27]

28 Put Together: A Digital Telephone System UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) 8kHz and 8-bit per sample for telephone speech => 64kbps Anti-aliasing filter before sampling Non-uniform quantization (e.g., through μ-law or A-law companding ~ signal compression-expansion) ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [28]

29 Summary UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) Speech production and analysis Spectrogram; Pitch, Formant Linear prediction model Speech coding Basic compression tools This week s Lab session: Design project#1 on Speech Next lecture: speech recognition ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [29]

30 Assignments UMCP ENEE408G Slides (created by M.Wu & R.Liu 2002) The Past, Present, and Future of Speech Processing IEEE Signal Processing Magazine, May 1998 Hard copy handout -- Read Section I, II, III Preparing for Friday Lab Lab manual will be posted online by Wednesday afternoon Go over Part I and II before coming to lab Already have background to do Part I & II Can play with Part III, IV-1, and V Start working on Part VI (Pocket PC) Team-up and Instructor and TA your team information by Thursday 5pm ENEE408G Capstone -- Multimedia Signal Processing (S'03) Lec1 Introduction 2/4/03 [30]