Percussion instruments

Most musical instrument classifiers focus on distinguishing different harmonic instruments such as the violin and the flute, whose sounds have very different characteristics. On the other hand, much less attention has been given to percussion instruments, especially if we consider the discrimination of instruments of the same type, like the cymbals in a drum kit. We have been developing classifiers that are able to distinguish this latter type of instruments. In particular we have been working with cymbal sounds and we are interested in modeling, classification, transcription and synthesis of these sounds. Some of the results appear in Cavaco and Almeida (IWSSIP 2012). Prospective (MSc and PhD) students interested in this topic may email me.

Harmonic instruments

Apart from the work with percussion instruments we have also been deveolping models that describe sounds from harmonic instruments, such as the flute, piano and guitar. Some of the results appear in Malheiro and Cavaco (INForum 2011).

Since today's digital content development triggered the massive use of digital music, an indexing process is very important to guarantee a correct organization of huge databases. While many supervised automatic music genre classifiers have been proposed, these will always be dependent on a previous manual labeling of the data. Alternatively, an unsupervised approach would not have this dependency and would be able to determine the genre of the music samples only based on their audio features. We have been developing unsupervised techniques for music genre classification. Some of the results appear in Barreira, Cavaco and Ferreira da Silva (EPIA 2011). (The figure on the left shows a similarity matrix from 165 music titles and 11 different genres.)

In particular, we are interested in having a Ph.D. student working in this area. Prospective PhD students interested in this topic may email me.

Models of sounds have proven useful in many fields, such as sound synthesis, sound recognition and identification of events or properties (like material or length) of the objects involved. However, developing such models is hard due to all the complexities of real sounds.

Natural sounds of the same type have a rich variability in their acoustic structure. For example, different impacts on the same rod can generate very different acoustic waveforms. In natural environments there is variability due to reverberation and background noise, but even when the sounds are recorded in anechoic conditions there is variability that is due to factors such as the slight variations in the impact force and location. (For instance, the figure on the left shows that, even though different impacts on the same rod have very similar spectra, the relative power and duration of the partials varies from one instance to the other. These differences cannot be explained by a simple variation in amplitude.) In spite of these variations, when the sounds are heard they are often perceived as almost identical, meaning that they have some common intrinsic structures.

We are developing data-driven methods for learning the intrinsic features that govern the acoustic structure of impact sounds. These methods require no a priori knowledge of the physics, dynamics and acoustics, and are used to create models of impact sounds that represent a rich variety of structure and variability in the sounds. For more details see Cavaco and Lewicki (JASA 2007).

Environmental sound recognition systems are intended to distinguish different categories of sounds, where sounds from different categories usually have very different spectral and temporal characteristics. A typical example of such categories is: door bells, waves, dog barking, whistle, footsteps, keyboard, etc. These sounds are not only produced by different types of objects but also by different types of events. We have been investigating the possibility of building sound recognizers (for environmental sounds and percussion instruments) that differ from the recognizers described above as they are intended to distinguish sounds produced by very similar objects and by the same type of event, such as impacts on metal rods (the image on the left shows that sounds from metal rods are separable) or sounds from a drum kit cymbals. Some of the results appear in Cavaco and Rodeia (ICISP 2010) and Cavaco and Almeida (IWSSIP 2012).

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