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 this work, we investigate the use of a multimodal search framework to deal with a representative formulation of the Sparse Component Analysis (SCA) problem. The proposed method, which employs an artificial immune network in the role of multimodal optimization tool, is explained and tested in different scenarios. The results are promising and indicate the relevance of using global search tool in SCA, as well as the soundness of the immune-inspired proposal.

Echo state networks represent a promising alternative to the classical approaches involving recurrent neural networks, as they ally processing capability, due to the existence of feedback loops within the dynamical reservoir, with a simplified training process. However, the existing networks cannot fully explore the potential of the underlying structure, since the outputs are computed via linear combinations of the internal states. In this work, we propose a novel architecture for an echo state network that employs the Volterra filter structure in the output layer together with the Principal Component Analysis technique. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. The proposed architecture has been analyzed in the context of the channel equalization problem, and the obtained results highlight the adequacy and the advantages of the novel network, which achieved a convincing performance, overcoming the other echo state networks, especially in the most challenging scenarios.

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.