In this paper, we analyze an inverse notch filter and present its application to F 0 (fundamental frequency) estimation. The inverse notch filter is a narrow band pass filter and it has an infinite impulse response. We derive the explicit forms for the impulse response and the sum of squared impulse response. Based on the analysis result, we derive a normalized inverse notch filter whose pass band area is identical to unit. As an application of the normalized inverse notch filter, we propose an F 0 estimation method for a musical sound. The F 0 estimation method is achieved by connecting the normalized inverse notch filters in parallel. Estimation results show that the proposed F 0 estimation method effectively detects F 0 s for piano sounds in a mid-range.
SUMMARYThe linear prediction method works in such a way that the difference (prediction residual) between the objective signal and the output generated by making the objective signal a linear combination of the input signals is uncorrelated. Hence, in this linear prediction method, the linear predictor generates zero for the signal without correlation and recombines the voice for that with correlation. If this characteristic is used, recombined voice with suppressed noise can be obtained as the output of the linear predictor for voice with superposed white noise without correlation. Noise suppression can be realized by using this characteristic of the linear predictor. In this paper, a noise suppression method based on this linear prediction is discussed. According to this method, a noise suppression method without process delay can be realized. Also, no a priori information on the power spectrum of the noise is needed in this method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.