Fundamental Frequency Estimation Algorithm Research Articles

An enhanced fast fundamental frequency estimator for three-phase electric aircraft grid

This article proposes a robust and enhanced three-phase fundamental frequency estimation algorithm for the electric aircraft grid. Unlike conventional frequency-locked loops, the digital signal processing technique-based frequency estimator proposed relies on the storage of five consecutive samples of the fundamental grid voltage signal. Furthermore, the ability to successfully operate at a low sampling frequency, i.e. 8 kHz, makes it sufficiently attractive as regards reducing the memory storage in a low-cost real-time controller. The proposed frequency estimator can additionally eliminate the negative effects of the DC-offset and the fundamental negative sequence components present in the grid signal without any additional effort. Also, the tuning efforts are reduced for the frequency variation range of 350–900 Hz owing to the existence of a single tuning gain parameter. The proposed algorithm has also been found to have a fast dynamic response, which has been experimentally validated through the use of a real-time digital controller.

PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise

We present PEFAC, a fundamental frequency estimation algorithm for speech that is able to identify voiced frames and estimate pitch reliably even at negative signal-to-noise ratios. The algorithm combines a normalization stage, to remove channel dependency and to attenuate strong noise components, with a harmonic summing filter applied in the log-frequency power spectral domain, the impulse response of which is chosen to sum the energy of the fundamental frequency harmonics while attenuating smoothly-varying noise components. Temporal continuity constraints are applied to the selected pitch candidates and a voiced speech probability is computed from the likelihood ratio of two classifiers, one for voiced speech and one for unvoiced speech/silence. We compare the performance of our algorithm with that of other widely used algorithms and demonstrate that it performs well in both high and low levels of additive noise.

Auditory Spectrum-Based Pitched Instrument Onset Detection

In this paper, a method for onset detection of music signals using auditory spectra is proposed. The auditory spectrogram provides a time-frequency representation that employs a sound processing model resembling the human auditory system. Recent work on onset detection employs DFT-based features describing spectral energy and phase differences, as well as pitch-based features. These features are often combined for maximizing detection performance. Here, the spectral flux and phase slope features are derived in the auditory framework and a novel fundamental frequency estimation algorithm based on auditory spectra is introduced. An onset detection algorithm is proposed, which processes and combines the aforementioned features at the decision level. Experiments are conducted on a dataset covering 11 pitched instrument types, consisting of 1829 onsets in total. Results indicate that auditory representations outperform various state-of-the-art approaches, with the onset detection algorithm reaching an F-measure of 82.6%.

Verified speaker localization utilizing voicing level in split-bands

This paper proposes a joint verification-localization structure based on split-band analysis of speech signal and the mixed voicing level. To address the problems in reverberant acoustic environments, a new fundamental frequency estimation algorithm is proposed based on high resolution spectral estimation. In the reconstruction of the distorted speech this information is utilized to reduce the side effect of acoustic noise on the voicing parts. A speaker verification system examines the features of the reconstructed speech in order to authorize the speaker before localization. This procedure prevents localization and beamforming for non-speech and specially the unwanted speakers in multi-speaker scenarios. The verification is implemented with the Gaussian Mixture Model and a new filtering scheme is proposed based on the voicing likelihood of each frequency band measured in the previous steps for efficient localization of the authorized speaker. The performance of the proposed VSL (verified speaker localization) front-end is evaluated in various reverberant and noisy environments. The VSL is utilized in the development of distant-talking automatic speech recognition by microphone array where the system can lock on a specific source and hence the recognition quality improves noticeably.

Error correction and accuracy scaling for a joint subtraction multiple fundamental frequency estimation algorithm

This talk presents an improved multiple fundamental frequency (F0) estimation algorithm, which is loosely based on a previously developed joint subtraction algorithm [A. Klapuri, Proc. 7th Int. Conf. on Music Information Retrieval, 216–221 (2006)]. The improved algorithm uses some auditory scene analysis techniques to supplement a maximum likelihood based F0 estimator to predict the best combination of F0s. The accuracy of this base algorithm is then improved by use of error detection/correction. Error rates of the algorithm were collected using random mixes of sounds gathered from the University of Iowa Theremin database of musical instrument samples, as well as mixes of continuous audio. The performance of both the base algorithm and the improved algorithm will be presented together. It was found that the improved algorithm decreases error rates by as much as 50%, compared to the base algorithm with minimal loss in computational efficiency. The trade-offs between computational efficiency and accuracy, tested via a tunable parameter built into the algorithm, will be discussed. Trade-offs between computational costs and accuracy as a function of the number of F0s will also be discussed.

ON-LINE FUNDAMENTAL FREQUENCY TRACKING METHOD FOR HARMONIC SIGNAL AND APPLICATION TO ANC

In this paper, a new indirect feedback active noise control (ANC) scheme based on the fundamental frequency estimation is proposed for systems with a harmonic noise. When reference signals necessary for feedforward ANC configuration are difficult to obtain, the conventional ANC algorithms for multi-tonal noise do not measure the reference signals but generate them with the estimated frequencies [1]. However, the beating phenomena, in which certain frequency components of the noise vanish intermittently, may make the adaptive frequency estimation difficult. The confusion in the estimated frequencies due to the beating phenomena makes the generated reference signals worthless. The proposed algorithm consists of two parts. The first part is a reference generator using the fundamental frequency estimation and the second one is the conventional feedforward control. We propose the fundamental frequency estimation algorithm using decision rules, which is insensitive to the beating phenomena. In addition, the proposed fundamental frequency estimation algorithm has good tracking capability and lower variance of frequency estimation error than that of the conventional cascade ANF method. We are also able to control all interested modes of the noise, even which cannot be estimated by the conventional frequency estimation method because of the poorS /N ratio. We verify the performance of the proposed ANC method through simulations for the measured cabin noise of a passenger ship and the measured time-varying engine booming noise of a passenger vehicle.

Real-time fundamental frequency estimation by least-square fitting

For real-time applications, a fundamental frequency estimation algorithm must be able to obtain accurate estimates from short signal segments. Characterization of the error function of fitting a sinusoid to the signal segment allows its spectrum to be deduced and the algorithm to be implemented efficiently. Musical signals are discussed in particular.