In this paper, a method for multi-pitch estimation of stereophonic mixtures of harmonic signals, e.g., instrument recordings, is presented. The proposed method is based on a signal model that includes the panning parameters of the sources in a stereophonic mixture, such as those applied artificially in a recording studio. If the sources in a mixture have different panning parameters, this diversity can be used to simplify the pitch estimation problem. The mixing parameters of the sources might be shared, resulting in a multi-pitch estimation problem, which is solved using an approach based on an expectation-maximization algorithm for Gaussian sources, where the fundamental frequencies and model orders are estimated jointly. The fundamental frequencies may be related, resulting in overlapping harmonics, complicating the estimation of the parameters. A codebook of harmonic amplitude vectors is trained on recordings of instruments playing single notes, and used when estimating the amplitudes of the mixture components. The proposed method is evaluated using stereophonic mixtures of instrument recordings and is compared to state-of-the-art transcription and multi-pitch estimation methods. Experiments show an increase in performance when knowledge about the panning parameters is taken into account. The proposed method provides a full parameterization of the components of the observed signal. Possible applications include instrument tuning, audio editing tools, modification of harmonic mixture components, and audio effects.

In this paper, a method for multi-pitch estimation of stereophonic mixtures of multiple harmonic signals is presented. The method is based on a signal model which takes the amplitude and delay panning parameters of the sources in a stereophonic mixture into account. Furthermore, the method is based on the extended invariance principle (EXIP), and a codebook of realistic amplitude vectors. For each fundamental frequency candidate in each of the sources, the amplitude estimates are mapped to entries in the codebook, and the pitch and model order are estimated jointly. The performance of the proposed method is evaluated using mixtures of real signals. Experiments show an increase in performance when knowledge about the panning parameters is utilized together with the codebook of magnitude amplitudes when compared to a state-of-the-art transcription method.

In this paper, a method for acoustic source localization using distributed microphone arrays based on time-differences of arrival (TDOAs) is presented. The TDOAs are used to estimate the location of an acoustic source using a recently proposed method, based on a 4D parameter space defined by the 3D location of the source, and the TDOAs. The performance of the proposed method for acoustic source localization is compared to the performance of a method based on generalized cross-correlation with phase transform (GCC-PHAT) using synthetic and speech signals with varying source position. Results show a decrease in the error of the estimated position when the proposed method is used.