A common approach to the detection of simultaneous musical notes in an acoustic recording involves defining a function that yields activation levels for each candidate musical note over time. These levels tend to be high when the note is active and low when it is not. Therefore, by applying a simple threshold decision process, it is possible to decide whether each note is active or not at a given time. Such a threshold, in general, is hard to set and has no physical meaning. In this paper, it is shown that the rhythmic characteristic of the musical signal may be used to obtain a suitable threshold. The proposed method for obtaining the threshold is shown to have a greater generalization capability over different databases.

In a musical signals, the spectral and temporal contents of instruments often overlap. If the number of channels is at least the same as the number of instruments, it is possible to apply statistical tools to highlight the characteristics of each instrument, making their identification possible. However, in the underdetermined case, in which there are fewer channels than sources, the task becomes challenging. One possible way to solve this problem is to seek for regions in the time and/or frequency domains in which the content of a given instrument appears isolated. The strategy presented in this paper explores the spectral disjointness among instruments by identifying isolated partials, from which a number of features are extracted. The information contained in those features, in turn, is used to infer which instrument is more likely to have generated that partial. Hence, the only condition for the method to work is that at least one isolated partial exists for each instrument somewhere in the signal. If several isolated partials are available, the results are summarized into a single, more accurate classification. Experimental results using 25 instruments demonstrate the good discrimination capabilities of the method.

A new approach to instrument identification based on individual partials is presented. It makes identification possible even when the concurrently played instrument sounds have a high degree of spectral overlapping. A pairwise comparison scheme which emphasizes the specific differences between each pair of instruments is used for classification. Finally, the proposed method only requires a single note from each instrument to perform the classification. If more than one partial is available the resulting multiple classification decisions can be summarized to further improve instrument identification for the whole signal. Encouraging classification results have been obtained in the identification of four instruments (saxophone, piano, violin and guitar).

We present a sequence of empirical methods to determine the number of sources in musical signals when only one channel is available. Rather than building evidence through a statistical model-based approach, we instead develop a carefully tuned and tested two-stage system that is able to function effectively even in extremely underdetermined conditions. A first, more general procedure accurately determines the number of sources that are not closely harmonically related, while the second stage subsequently detects the presence of any remaining sources. The main advantages of this approach lie in its avoidance of the restrictive assumptions that can accompany more complex models in underdetermined cases, and in its use of robust heuristics to identify and exploit as much source-specific information as possible. These features make it possible to address even the most difficult cases in which sources are closely harmonically related, or even share the same fundamental frequency. We report an overall accuracy of nearly 80% on average, using both random and harmonically related mixtures of one to six sources taken from two widely available musical instrument databases-a notable result that demonstrates both the efficiency and the robustness of our proposed procedure.