- The paper introduces a novel Summation of Residual Harmonics (SRH) method that robustly estimates pitch and detects voicing in challenging noisy conditions.
- It employs auto-regressive modeling and inverse filtering to extract a residual signal that minimizes vocal tract influences and background noise.
- Experimental evaluations demonstrate that SRH significantly outperforms six state-of-the-art pitch tracking algorithms by reducing error metrics in noisy scenarios.
Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics
This paper addresses the persistent challenge in the domain of speech processing regarding the robust estimation of pitch contours, particularly in noisy environments. The authors introduce a novel approach that leverages harmonic information extracted from the residual signal for both pitch estimation and voicing decisions. This method, termed the Summation of Residual Harmonics (SRH), differentiates itself from traditional pitch estimation techniques by focusing on the spectrum of the residual signal rather than the direct speech signal. This distinction is crucial as it mitigates the effects of vocal tract resonances and noise, thus demonstrating significant robustness under adverse conditions.
The theoretical framework is grounded in auto-regressive modeling of the spectral envelope to obtain a residual signal by inverse filtering the speech signal. This process effectively eliminates prominent noise elements and vocal tract influences. Subsequently, the analysis of the amplitude spectrum of this residual signal allows for the detection of fundamental frequency (F0) through identification of harmonic peaks. The SRH criterion is defined mathematically, taking into consideration the summation of harmonic peaks while reducing confounding influences from even harmonics by introducing a subtraction term in the formulation.
The experimental section of the paper involves an extensive quantitative evaluation using the Keele and CSTR databases, along with the additional use of the APLAWD database for parameter optimization. The proposed SRH method is rigorously compared against six state-of-the-art pitch tracking algorithms, namely Get_F0, SHRP, TEMPO, AC, CC, and YIN. The evaluation, employing both clean and noisy speech signals, utilizes critical metrics such as Voicing Decision Error (VDE), Gross Pitch Error (GPE), Fine Pitch Error (FPE), and F0 Frame Error (FFE) to establish performance benchmarks.
Empirical results exhibit that SRH is particularly effective in noisy environments, consistently outperforming traditional methods in 9 out of 10 noisy test cases. The paper highlights a notable reduction in the overall FFE when employing SRH across various noise types and gender-based voice datasets. This robustness is attributed to SRH’s focused analysis on residual harmonics, which inherently reduces the noise impact compared with other harmonic or autocorrelation-based methods.
Furthermore, the paper presents comprehensive parameter optimization for SRH, ensuring balanced performance across diverse conditions. For instance, a LPC order of 12 and a frame length of 100 ms were identified as optimal for maintaining low FFE and retaining the ability to track rapidly-varying pitch contours effectively.
In conclusion, the contributions of this paper are twofold: a novel methodological advance in pitch tracking via residual harmonics and robust empirical evidence of improved performance in noisy conditions. This work has practical implications, particularly in the enhancement of speech processing systems deployed in variable and challenging auditory environments. The theoretical contributions also provide a foundation for further exploration into residual signal utilization, potentially extending to more complex aspects of speech analysis and synthesis. Future research could explore dynamic programming enhancements for further reducing pitch estimation errors and integrating this method in real-time speech processing applications to assess its effectiveness in a wide array of operational contexts.