Sergio Krakowski

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There is an increasing need for automatically classifying sounds for MIR and interactive music applications. In the context of supervised classification, we describe an approach that improves the performance of the general bag-of-frame scheme without loosing its generality. This method is based on the construction and exploitation of specific audio(More)
There is an increasing need for automatically classifying sounds for MIR and interactive music applications. In the context of supervised classification, we conducted experiments with so-called analytical features, an approach that improves the performance of the general bag-of-frame scheme without loosing its generality. These analytical features are(More)
This work aims to evaluate the effectiveness of EDS as a tool to automatically extract descriptors for real-world problems, such as melody extraction, chord recognition, and sound classification, comparing its performance and development time to traditional approaches. Each of these problems constitutes a case study, and along with the comparative results(More)
This work aims to evaluate the effectiveness of EDS as a tool to automatically extract descriptors for real-world problems, such as melody extraction, chord recognition, and sound classification, comparing its performance and development time to traditional approaches. Each of these problems constitutes a case study, and along with the comparative results(More)
This work aims to evaluate the effectiveness of EDS as a tool to automatically extract descriptors for real-world problems, such as melody extraction, chord recognition, and sound classification, comparing its performance and development time to traditional approaches. Each of these problems constitutes a case study, and along with the comparative results(More)
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