Frame Length Dependency for Fundamental Frequency Extraction in Noisy Speech

The fundamental frequency plays a significant part in understanding and perceiving the pitch of a sound. The pitch is a fundamental attribute employed in numerous speech-related works. For fundamental frequency extraction, several algorithms have been developed which one to use relies on the signal’s characteristics and the surrounding noise. Thus, the algorithm’s noise resistance becomes more critical than ever for precise fundamental frequency estimation. Nonetheless, numerous state-of-the-art algorithms face struggles in achieving satisfying outcomes when confronted with speech recordings that are noisy with low signal-to-noise ratio (SNR) values. Also, most of the recent techniques utilize different frame lengths for pitch extraction. From this point of view, This research considers different frame lengths on male and female speech signals for fundamental frequency extraction. Also, analyze the frame length dependency on the speech signal analytically to understand which frame length is more suitable and effective for male and female speech signals specifically. For the validation of our idea, we have utilized the conventional autocorrelation function (ACF), and state-of-the-art method BaNa. This study puts out a potent idea that will work better for speech processing applications in noisy speech. From experimental results, the proposed idea represents which frame length is more appropriate for male and female speech signals in noisy environments.

Adaptive Kalman filter for MEMS IMU data fusion using enhanced covariance scaling

MEMS(micro-electro-mechanical-system)IMU(inertial measurement unit)sensors are characteristically noisy and this presents a serious problem to their effective use.The Kalman filter assumes zero-mean Gaussian process and measurement noise variables,and then recursively computes optimal state estimates.However,establishing the exact noise statistics is a non-trivial task.Additionally,this noise often varies widely in operation.Addressing this challenge is the focus of adaptive Kalman filtering techniques.In the covariance scaling method,the process and measurement noise covariance matrices Q and R are uniformly scaled by a scalar-quantity attenuating window.This study proposes a new approach where individual elements of Q and R are scaled element-wise to ensure more granular adaptation of noise components and hence improve accuracy.In addition,the scaling is performed over a smoothly decreasing window to balance aggressiveness of response and stability in steady state.Experimental results show that the root mean square errors for both pith and roll axes are significantly reduced compared to the conventional noise adaptation method,albeit at a slightly higher computational cost.Specifically,the root mean square pitch errors are 1.1∘under acceleration and 2.1∘under rotation,which are significantly less than the corresponding errors of the adaptive complementary filter and conventional covariance scaling-based adaptive Kalman filter tested under the same conditions.