An iterative subspace-based multi-pitch estimation algorithm
نویسندگان
چکیده
منابع مشابه
An iterative subspace-based multi-pitch estimation algorithm
In this paper, we present an iterative method for estimation of pitches from signals containing multiple sources using subspace techniques. The resulting estimator is termed Iterative Harmonic MUltiple SIgnal Classification (I-HMUSIC). Different modifications of I-HMUSIC are proposed that improve upon the classical MUSIC algorithm, including a computationally efficient method for noise subspace...
متن کاملJoint DOA and multi-pitch estimation based on subspace techniques
In this article, we present a novel method for high-resolution joint direction-of-arrivals (DOA) and multi-pitch estimation based on subspaces decomposed from a spatio-temporal data model. The resulting estimator is termed multi-channel harmonic MUSIC (MC-HMUSIC). It is capable of resolving sources under adverse conditions, unlike traditional methods, for example when multiple sources are impin...
متن کاملRNN-BLSTM Based Multi-Pitch Estimation
Multi-pitch estimation is critical in many applications, including computational auditory scene analysis (CASA), speech enhancement/separation and mixed speech analysis; however, despite much effort, it remains a challenging problem. This paper uses the PEFAC algorithm to extract features and proposes the use of recurrent neural networks with bidirectional Long ShortTerm Memory (RNN-BLSTM) to m...
متن کاملSemi-Blind Channel Estimation based on subspace modeling for Multi-user Massive MIMO system
Channel estimation is an essential task to fully exploit the advantages of the massive MIMO systems. In this paper, we propose a semi-blind downlink channel estimation method for massive MIMO system. We suggest a new modeling for the channel matrix subspace. Based on the low-rankness property, we have prposed an algorithm to estimate the channel matrix subspace. In the next step, using o...
متن کاملRobust and efficient pitch estimation using an iterative ARMA technique
In this article, we propose an innovative way of estimating pitch from speech waveform data, using an iterative ARMA technique that efficiently estimates multiple frequency components of a time series. Additionally, the harmonic structure of voiced speech and the smoothness of its pitch period are incorporated into the iterative ARMA technique, and this novel integration results in an efficient...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Signal Processing
سال: 2011
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2010.06.010