A Full Frequency Masking Vocoder for Legal Eavesdropping Conversation Recording R. F. B. Sotero Filho, H. M. de Oliveira (qPGOM), R. Campello de Souza Signal Processing Group, Federal University of Pernambuco – UFPE

Abstract: New approach for a vocoder Based on: full frequency masking by octaves Useful to save bandwidth (applications requiring intelligibility) Recommended for: legal eavesdropping of long conversations.

Introduction Vocoder = contraction from voice encoder: waveform not recreate the original waveform in appearance, (but it should be perceptually similar to it) first described by Homer Dudley at Bell Telephone Laboratory in 1939 Parameters are extracted from the spectrum and updated every ms Properties of voice: limitation of the human auditory system physiology of the voice generation process

Psycho-Acoustics of the Human Auditory System Frequency Masking: Masking in frequency or "reduced audibility of a sound due to the presence of another" Insensitivity to the phase: The human ear has little sensitivity to the phase of signals

Simplification of the spectrum via frequency masking For each voice segment: FFT of blocklength 160 (frame of 20 ms) The spectrum is segmented into regions of influence (octaves). The range Hz is removed. 64 Hz-128 Hz, 128 Hz-512 Hz, and so on. Each spectral sample corresponds to a multiple of 50 Hz

Table 1. Number of spectral lines per octave (DFT of length N=160, sample rate 8 kHz) Octave (Hz)# spectral samples/octave A total of 79 frequencies (DFT with N=160) is reduced to 4 survivors! (holding less than 5% of the spectral components).

Figure 1. The spectrum of a voice frame computed by the FFT: a)Original spectrum b) Simplified full-masking spectrum This technique is called full frequency masking.

Signal synthesis via spectral filling The beta distribution is a probability distribution defined over 0≤x≤1, characterized by a pair of parameters α and β : P(x)=1/B(α,β) x (α-1) (1-x) (β-1), 1<α,β<+∞, whose normalized factor is B(α,β)=(Γ(α)Γ(β))/(Γ(α+β)), where Γ(.) is the generalized Euler factorial function and B(.,.) is the Beta function. Figure 2. Envelope shape of survivor tone different parameters α and .

By making the fitting: new mode = (α-1)/(α+β-2) (f M - f m )+ f m. upper limit is equivalent to the difference between the normalized cutoff frequency exceeding (f M ) and lower (f m ) of each octave, i.e., f M - f m. To fulfill the spectral algorithm each frame: P(x)= 1/( f M - f m ) (α+β-2) (x- f m ) (α-1) (f M -x) (β-1).

( – piece of speech from radio) A few audio files generated by this vocoder are available at the URL (vocoder with Hamming windowing) Figure 3. Full masking and spectral filling

Quantization and Coding of Speech Signals The maximum excursion of the full-spectrum was divided into 256 intervals of equal length, each represented by one byte. No negative samples to be quantized => the quantizer cannot be bipolar. Table 2. Bit allocation in a voice frame (20 ms). The required number of bits is expressed as A + P, where A is the number of bits for spectral line amplitude and P the number of bits to express the relative position within the octave. Relevant octave#possible survivor componentsBits A+P #1 ( Hz) #2 ( Hz) #3 ( Hz) #4 ( Hz)

Each voice frame needs 50 bits (18 for identifying positions and 32 for identifying masking tones), The vocoder rate is 50 bits/20 ms=2.5 kbps The binary format.voz The representation of a voice frame in this format (extension.voz): The 50 bits are distributed into four sub blocks, indicating the value of the spectral sample followed by its respective position in the spectrum. The voice files registered in the.wav format are converted to this binary format, by a Matlab routine.

Figure 4. Frame of files in the format.voz (20 ms). Table 3. MOS scores for the voice signals synthesized by four different techniques Vocoder techniqueMOS score Synthesized signals with no spectral filling3.0 Vocoder signals reconstructed via beta spectral filling technique2.5 Synthesized voice signals combining 1 and 2 techniques (linear)2.8 Voice signals from item 2, but with an extra Hamming windowing3.0 Intelligibility and voice quality versus bit rate Voice quality is estimated using the "Mean Opinion Score (MOS)"

Conclusions New vocoder: voice signal using fewer samples of the spectrum. Voice (acceptable quality) at a rate of a few kbits/s. A new technique of spectral filling: not helpful in improving the voice quality, but naturalness APPLICATIONS maintenance voice channels in large plants speaker recognition system monitoring voice conversation from authorized eavesdropping THAT’S ALL FOLKS! TKS.

Pre-signal processing Shannon sampling theorem (a signal band limited to f m Hz is sampled at a rate of at least 2f m equally spaced samples per second). LPF. Voice Segmentation and Windowing partition of the speech signal into pieces (stationary frames): (~ ms). Hamming window chosen due to softness at the edges. Pre-emphasis -6dB/octave, radiated from the lips during speech. This spectral distortion can be eliminated by applying a filter response approximately +6 dB/octave y(n)= x(n)-a.x(n-1), for 1 ≤ n < M, where M is the number of samples of x(n), y(n) is the emphasized signal and the constant "a" is normally set 0.95.