This paper presents a method for high-resolution fundamental frequency$(F_{0})$estimation based on subspaces decomposed from a frequency-selective data model, by effectively splitting the signal into a number of subbands. The resulting estimator is termed frequency-selective harmonic MUSIC (F-HMUSIC). The subband-based approach is expected to ensure computational savings and robustness. Additionally, a method for automatic subband signal activity detection is proposed, which is based on information-theoretic criterion where no subjective judgment is needed. The F-HMUSIC algorithm exhibits good statistical performance when evaluated with synthetic signals for both white and colored noises, while its evaluation on real-life audio signal shows the algorithm to be competitive with other estimators. Finally, F-HMUSIC is found to be computationally more efficient and robust than other subspace-based$F_{0}$estimators, besides being robust against recorded data with inharmonicities.

In this paper, we present a novel method for joint estimation of the order and fundamental frequency of a set of harmonically related sinusoids. This method uses a subband based approach to estimate the involved parameters using subspace techniques, and the resulting algorithm is termed frequency-selective harmonic MUSIC (F-HMUSIC). The performance of F-HMUSIC is evaluated and compared to both harmonic MUSIC (HMUSIC) and Cramer-Rao lower bound (CRLB). Especially, in a low signal-to-noise ratio (SNR) with colored noise scenarios, where F-HMUSIC outperforms HMUSIC. F-HMUSIC is concluded to be more computationally efficient and more robust against colored noise than other subspace based fundamental frequency estimators.

The problem of estimating frequencies of sinusoids in noise has been studied intensively by the signal processing community during the last decades. Traditionally high resolution subspace-based techniques suffer from high computational complexity, and generally sensitive to the colored noise. We present here a frequency-domain based subspace parameter estimation algorithm termed frequency-selective MUltiple SIgnal Classification (F-MUSIC) that is based on the signal and noise subspace orthogonality property. The method is computationally efficient in providing estimates in the selected subband compared to the classic MUSIC. The performance of F-MUSIC is evaluated and compared to both MUSIC and Cramer-Rao lower bound (CRLB). In a low signal to noise ratio (SNR) with colored noise scenarios, F-MUSIC outperforms MUSIC.

We present an approach for the extraction of parameters of a damped complex exponential model from a spectrogram modified by a binary mask.The parameters are estimated by a frequency domain based methods using subspace techniques, where the core algorithm is F-ESPRIT.The sub-band defined by the binary mask provides a reduced number of DFT-samples for the parameter extractions, which results in a computational efficient scheme with high parameter estimation accuracy.The proposed synthesis system has synthesis performance comparable to the so-called LSEE-MSTFT.The estimated parameters can be used in many applications such as audio/speech coding, pitch estimation and pitch scale modification.