Extending Radio Broadcasting Semantics through Adaptive Audio Segmentation Automations

Abstract

The present paper focuses on adaptive audio detection, segmentation and classification techniques in audio broadcasting content, dedicated mainly to voice data. The suggested framework addresses a real case scenario encountered in media services and especially radio streams, aiming to fulfill diverse (semi-) automated indexing/annotation and management necessities. In this context, aggregated radio content is collected, featuring small input datasets, which are utilized for adaptive classification experiments, without searching, at this point, for a generic pattern recognition solution. Hierarchical and hybrid taxonomies are proposed, firstly to discriminate voice data in radio streams and thereafter to detect single speaker voices, and when this is the case, the experiments proceed into a final layer of gender classification. It is worth mentioning that stand-alone and combined supervised and clustering techniques are tested along with multivariate window tuning, towards the extraction of meaningful results based on overall and partial performance rates. Furthermore, the current work via data augmentation mechanisms contributes to the formulation of a dynamic Generic Audio Classification Repository to be subjected, in the future, to adaptive multilabel experimentation with more sophisticated techniques, such as deep architectures.

Exploring music listening patterns: an online survey

“…It could be helpful for radio stations and streaming platforms to deliver the best product to their clients. Moreover, future studies should be devoted to the media management organization and their mechanisms [12]. It should also be taken into account that individual and freelancing producers are involved in media creation and become the central core contributors to the so-called user-generated content.…”

“…It should also be taken into account that individual and freelancing producers are involved in media creation and become the central core contributors to the so-called user-generated content. In addition, Kotsakis et al [12] pointed out that the audio repository should be dynamic for effective broadcasted data management, as well as for radio content description and archiving purposes.…”

“Give me happy pop songs in C major and with a fast tempo”: A vocal assistant for content-based queries to online music repositories

“…It is worth noticing that the general structure of the proposed system could be applied in domains di↵erent from the musical one, such as repositories of other audio content such as speech, non-musical sounds and the combination thereof [41].…”