In this paper we compare four different pitch tracking algorithms - autocorrelation, cepstrum, harmonic product spectrum, and a new method based on the modulation spectrum - and investigate their fitness for noisy environments. From each tracker, possible ƒ0candidates in every time window are stored over a 10-frame interval, and the best contour is computed through a Viterbi search. The audio data comprises speech samples recorded in a moving car with a microphone setup commonly used for car speech applications like speakerphones. To see how performances deteriorate with increasing noise level, a second audio test set is prepared by blending clean speech recordings and noise at different signal-ta-noise ratios.

A new class of codes for data compression is described that combines permutations with the fast Hadamard transform (FHT). It was invented for digital speech compression based on linear predictive coding (LPC), but may be useful for other data compression applications. One particular code with rate\frac{1}{2}is considered: a16-bit code for a block length of32samples. All coding and decoding steps are fast, so that real-time applications with cheap hardware can be anticipated.

The reasons underlying the success of predictive coding are outlined. Proper attention to auditory perception and masking of the quantizing noise have resulted in coders with high quality at 0.5 bits/residual sample. Ever more powerful chips have transported these basic principles into the realm of real-time reality. For sustained progress, current research should focus on 1. The effects of dynamic masking: during and immediately following rapid transitions in the speech spectrum the ear seems more tolerant to rough coding. The saving in average information rate could be substantial. 2. Better algebraically structured codes: codes are needed that are fast, simple to implement and allow the incorporation of subjective criteria. 3. Efficient coding of the parameters: because of the great success with coding the residual, sparse representation of the predictor coefficients is now paramount.

We describe in this paper a code-excited linear predictive coder in which the optimum innovation sequence is selected from a code book of stored sequences to optimize a given fidelity criterion. Each sample of the innovation sequence is filtered sequentially through two time-varying linear recursive filters, one with a long-delay (related to pitch period) predictor in the feedback loop and the other with a short-delay predictor (related to spectral envelope) in the feedback loop. We code speech, sampled at 8 kHz, in blocks of 5-msec duration. Each block consisting of 40 samples is produced from one of 1024 possible innovation sequences. The bit rate for the innovation sequence is thus 1/4 bit per sample. We compare in this paper several different random and deterministic code books for their effectiveness in providing the optimum innovation sequence in each block. Our results indicate that a random code book has a slight speech quality advantage at low bit rates. Examples of speech produced by the above method will be played at the conference.

Digital coders invariably introduce errors into analog signals such as speech. The importance of adjusting the spectrum of the error so as to minimize the subjective distortion is now well recognized. The optimum noise spectrum for minimum subjective distortion is in general non-flat. Predictive coders use instantaneous quantizers with feedback around the quantizer to shape the quantizing noise spectrum. However, for non-flat spectra, instantaneous quantizers are suboptimal. We have therefore considered and reported on non-instantaneous digital speech coders using tree-search procedures to determine the optimal innovation sequence. In this paper, we describe tree-search procedures to determine optimal block codes. We discuss both random and special block codes based on optimum packing of Voronoi regions on unit spheres in multi-dimensional spaces.

In previous papers on digital coding we have stressed the importance of taking proper account of the masking properties of the human ear in order to minimize the subjective loudness of the quantizing noise. The resulting optimal quantizing noise spectrum is in general not flat and requires the use of noise-shaping filters. This masking of the quantizing noise by the speech signal itself has allowed us to use very low bit rates (less than 1 bit/sample for the prediction residual in aa adaptive predictive coder) while maintaining high speech quality. However, if the low bit rates are realized by a (coarse) instantaneous qnantizer, the quantizing error is not white and the noise-shaping filter (in the feedback loop around the quantizer) does not produce the intended noise spectrum. In this paper, we therefore describe non-instantaneous, tree-coding methods that allow the attainment of even lower bit rates (near the theoretical rate-distortion limit) with the precise optimum noise spectrum.

Adaptive predictive coding of speech signals at bit rates lower than 10 kbits/sec often requires the use of 2-level (1 bit) quantization of the samples of the prediction residual. Such a coarse quantization of the prediction residual can produce audible quantizing noise in the reproduced speech signal at the receiver. This paper describes a new method of quantization for improving the speech quality. The improvement is obtained by center clipping the prediction residual and by fine quantization of the high-amplitude portions of the prediction residual. The threshold of center clipping is adjusted to provide encoding of the prediction residual at a specified bit rate. This method of quantization not only improves the speech quality by accurate quantization of the prediction residual when its amplitude is large but also allows encoding of the prediction residual at bit rates below 1 bit/sample.

Adaptive shaping of the quantizing noise spectrum is essential for optimal subjective performance of speech coders. In this paper, we discuss detailed procedures for minimizing the subjective loudness or audibility of quantizing noise in linear predictive coders. The procedure takes account of the frequency analysis by the human ear and its auditory masking properties.

Predictive coding methods attempt to minimize the r.m.s. error in the coded signal. However, the human ear does not perceive signal distortion on the basis of r.m.s. error regardless of its spectral shape relative to the signal spectrum. Specifically, for speech signals, the locations of the formant frequencies and their rates of change with time influence the audibility, and thus the subjective distortion of any quantizing noise. In this paper, methods for reducing the subjective distortion in predictive coders for speech siganls are described and evaluated. Improved speech quality is obtained a) by efficient removal of formant and pitch related redundant structure of speech before quantizing and b) by effective masking of the quantizer noise by the speech signal.

This correspondence considers the problem of how to adjust the phase angles of a periodic signal with a given power spectrum to minimize its peak-to-peak amplitude. This "peak-factor problem" arises in radar, sonar, and numerous other applications. However, in spite of the wide-spread interest it has evoked, the peak-factor problem has so far defied solution except in cases where the number of spectral components is small enough to permit an effectively exhaustive search of all phase angle combinations. In this correspondence, a formula for the phase angles is derived that yields generally low peak factors, often comparable to that of a sinusoidal signal of equal power. A formula is also derived for the case in which the phase angles are restricted to 0 and\pi. The latter formula is applicable to the problem of constructing binary sequences of arbitrary length with Iow autocorrelation coefficients for nonzero shifts.

In spite of their great potential for bandwidth saving in long distance telephony, vocoders have not found wide-spread acceptance. Two major problems have retarded their application. First is their strong electrical accent. Second is the so-called "pitch problem;" namely, deducing the nature of the talker's vocal excitation from his speech waveform. The reliability of this deduction and measurement depends critically upon high input speech-to-noise ratio, particularly between 50 and 200 cps. In many communication situations, this requirement precludes satisfactory operation. This limitation can be removed by a new method known as "voice excitations' which eliminates the necessity for a decision-making pitch detector. The principal advantage of voice excitation is its insensitivity to input signal-to-noise ratio and equalization. A voice-excited vocoder (VEV) with a 720 cps (250-970 eps) baseband and 17 spectrum channels low-passed to 25 cps each, covering the band 970-3700 cps, has been built and evaluated. The test shows an average PB-word intelligibility of 86%, compared to 92% for input speech of the same bandwidth, both with an 18 db signal-to-noise ratio. Quality tests indicate that listeners rate VEV speech "as good as" the input in about 90% of the test utterances. 0nly 19% of conventional vocoder utterances were so considered. The vocoder performed about equally well for each of the 12 speakers in the quality test. Voice-discrimination tests indicate that voice identity is well preserved. Crucial factors influencing the remade speech quality are the accuracy of spectral flattening and the impulse response of the analyzer low-pass filters. These results indicate that the principle of voice excitation provides the key to practical speech bandwidth reduction.

The problem of compatible single sideband transmission is given a precise mathematical formulation. The problem is then recognized as a combination of two problems, each of which has previously been solved. Iterative methods are required for the solution of the second problem. Examples are given illustrating one of two iteration schemes.