Evaluating a New Algorithm for Multi-Talker Babble ...
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Evaluating a New Algorithm for Multi-Talker Babble ...
Overview: ⢠Performance with a cochlear implant (CI) in noise is generally poor. ⢠Previous noise reduction techniques tend to work poorly in non-stationary ...
Evaluating a New Algorithm for Multi-Talker Babble Noise Reduction Using Q-Factor Based Signal Decomposition Roozbeh Soleymani, Ivan W. Selesnick, Natalia Stupak, David M. Landsberger Overview: Performance with a cochlear implant (CI) in noise is generally poor. Previous noise reduction techniques tend to work poorly in non-stationary noise (e.g., multi-talker babble). We propose a new algorithm to improve speech in multi-talker babble that could be implemented into a speech processor. Results demonstrate a consistent and significant benefit in intelligibility and sound quality.
- Added noise is 6-Talker babble, SNR=0dB - Each graph number corresponds to a block in block diagram Figure 3.
A pulse with a high Q-factor (HQF) exhibits more sustained oscillatory behavior. A pulse with a low Q-factor (LQF) exhibits less sustained oscillatory behavior.
SNR Estimation
Noisy Speech HQF Component
Noisy Speech
Noisy Speech Cleaned HQF
Output Processed Speech
0
1 Time (Sec.)
2
1 Time (Sec.)
2
Noise Free Speech HQF Component
Noisy Speech LQF Component
Noisy Speech HQF Component
Noisy Speech LQF Component
Subjects: 7 Advanced Bionics Fidelity 120 or Optima users with Medium Clear Voice. Stimuli: IEEE sentences in multi-talker babble SNR 0, 3, 6, or 9dB, Processed or Unprocessed Evaluating Intelligibility: Words correct were measured for 20 sentences for each of the 8 conditions (two processing conditions and 4 SNRs) in a randomized order. Evaluating Quality: A MUshra test was used to determine sound quality for all 8 conditions relative to a speech in quiet reference. The low quality anchor was 6-talker babble noise without speech. The process was repeated for 5 sentences. Results: For all subjects, intelligibility and quality improved. Intelligibility improves 10 to 30% in 6-talker babble. Sound Quality improves between 15 and 30 points. Conclusions: The new algorithm might greatly improve performance in realistic noisy environments (i.e. a cocktail party). We are working on implementing a real time implementation of the new algorithm.
Noise Free (Clean) Speech
Noisy Speech
Output Processed Speech
Noisy Speech Cleaned HQF
- Time axis (Horizontal) : 0 to 2 (sec) - Freq. axis (Vertical) : 0 to 6000Hz - Added noise is 6-Talker babble, SNR=0dB - Each graph number corresponds to a block in block diagram Figure 3. 0
1 Time (Sec.)
2
OUTPUT
0
1 Time (Sec.)
0
2
1 Time (Sec.)
0
2
1 Time (Sec.)
2
0
1 Time (Sec.)
2
Results
Improvement found at ALL SNRs
=
SNR>12dB
1
0
Noise Free Speech LQF Component
Spectral cleaning and re-composition
Each number in the block diagram corresponds to a plot in figures 1 and 2 signal
Noisy Speech Residual Component
- Time axis (Horizontal) : 0 to 2 (sec)
𝑓𝑐 . 𝐵𝑊
Spectrograms of the noise
free, noisy and de-noised signals and
Noisy Speech LQF Component
What is the Q-factor? The Q-factor of a pulse is defined as the ratio of its center frequency to its bandwidth:
Figure 2.
noisy and de-noised signals and their
Algorithm summary (See Figure 3): Detect signal to noise ratio to determine how aggressive the de-noising will be. Decompose signal into three components: a low Q-factor (LQF), high Q-factor (HQF), and residual noise components using a sparse optimization wavelet method. (see “What is the Q-factor?”) The low Q-factor component is used as a template to further de-noise the high Q-factor component. The de-noised high Q-factor signal is added to the low Q-factor component to create the de-noised output.
