The assessment of music performances in most cases takes into account the underlying musical score being performed. Likewise, the registration of their sound quality should be done regularly to have standard parameters for comparison. Young wind instrumentalists should have dental impressions of their teeth made, so their dentist has the most reliable anatomy of the natural teeth in case of an orofacial trauma. This investigation supports the findings that the intra-oral appliance which occupies less volume is the best solution in terms of sound quality. Pitch deviations may result from the different intra-oral appliances due to the alteration of the mouth cavity, respectively, the area occupied and modification/interaction with the anatomy. A linear frequency response microphone was adopted for precision measurement of pitch, loudness, and timbre descriptors. Objectively assessing the sound quality of the trumpet player with these new devices in terms of its spectral, temporal, and spectro-temporal audio properties. Three intraoral appliances were manufactured: A Hawley appliance with a central expansion screw and two central incisors (1), trumpet edentulous anterior tooth appliance (2) and a customized splint (3) were designed as part of the rehabilitation procedure. A 13-year boy suffered the avulsion of tooth 11 and 21, lost at the scenario. The occurrence of an orofacial trauma can originate health, social, economic and professional problems. We provide explanatory visualisations of scattering coefficients for each technique and verify the system over three additional datasets with various instrumental and vocal techniques: VPset, SOL, and VocalSet. Once trained on the proposed scattering representations, a support vector classifier achieves state-of-the-art results. To evaluate the proposed methodology, we create a new dataset containing full-length Chinese bamboo flute performances (CBFdataset) with expert playing technique annotations. We analyse seven playing techniques: vibrato, tremolo, trill, flutter-tongue, acciaccatura, portamento, and glissando. Two adaptive scattering features are presented: frequency-adaptive scattering and direction-adaptive scattering. We propose the adaptive scattering transform, which refers to any scattering transform that includes a stage of data-driven dimensionality reduction over at least one of its wavelet variables, for representing playing techniques. To address this problem, our paper develops a general framework for playing technique recognition. Yet, current research in music signal analysis suffers from a scarcity of computational models for playing techniques, especially in the context of live performance. Playing techniques contain distinctive information about musical expressivity and interpretation.
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