Audio on Demand

O podcast é uma mídia que permite a distribuição de informação em áudio por demanda. Em sintonia com as mudanças nas relações de poder do consumidor na era digital, o indivíduo é livre para consumir o conteúdo quando, onde e no dispositivo que desejar. O propósito deste trabalho é analisar um dos gêneros presentes nessa mídia: os programas não ficcionais de longo formato e alta produção, como documentários ou reportagens especiais em áudio. São variadas as possibilidades, mas, no geral, o objetivo é combinar informação e credibilidade jornalística com formas criativas e doses de subjetividade. Aproveitando­se dos recursos do áudio e de mecanismos de storytelling, realiza­se uma abordagem acessível, porém, não superficial de um assunto ou história, que pode ou não ser também voltada ao entretenimento. Existente em rádios de países do hemisfério norte há décadas, o modelo foi para o podcast e crescia estavelmente. Serial, um programa americano do tipo lançado em 2014, obteve enorme sucesso e, combinado a outros fatores, ajudou a alterar o cenário da mídia, a começar pelos Estados Unidos, onde o podcast passa por um ressurgimento criativo e econômico.

This presentation attempts to examine the social and communicational changes that new technologies and especially internet technology has over “traditional” media, such as radio and communication practices. More than ninety years after the world’s first station was founded, radio is still the most pervasive, accessible, affordable, and flexible mass medium available. Over the past few years a number of experiments blending independent local radio and the Internet are creating new models of communication. A newer generation of audience evolved through this amalgamation of older and newer technology. Interfaces that allow the user to select through a wide range of music styles and artists transform the radio to a personal music player with the quality elements of the old medium and the personalized music choices of the audience.

This article studies a vital issue in wireless communications, which is the transmission of audio signals over wireless networks. It presents a novel interleaver scheme for protection against error bursts and reduction the packet loss of the audio signals. The proposed technique in the article is the chaotic interleaver; it is based on chaotic Baker map. It is used as a randomizing data tool to improve the quality of the audio over the mobile communications channels. A comparison study between the proposed chaotic interleaving scheme and the traditional block and convolutional interleaving schemes for audio transmission over uncorrelated and correlated fading channels is presented. The simulation results show the superiority of the proposed chaotic interleaving scheme over the traditional schemes. The simulation results also reveal that the proposed chaotic interleaver improves the quality of the received audio signal. It improves the amount of the throughput over the wireless link through the packet loss reduction. Figure 9 Received audio signal of file-1 waveform over a correlated fading channel (V c = 10 mile/h) at SNR = 20 dB. (a) No interleaving. (b) Bit-level interleaving. (c) Convolutional interleaving.

The aim of the study is to transpose and extend to a set of environmental sounds the notion of sound descriptors usually used for musical sounds. Four separate primary studies dealing with interior car sounds, air-conditioning units, car horns, and closing car doors are considered collectively. The corpus formed by these initial stimuli is submitted to new experimental studies and analyses, both for revealing metacategories and for defining more precisely the limits of each of the resulting categories. In a second step, the new structure is modeled: common and specific dimensions within each category are derived from the initial results and new investigations of audio features are performed. Furthermore, an automatic classifier based on two audio descriptors and a multinomial logistic regression procedure is implemented and validated with the corpus.

Abstract The availability of haptic interfaces in music content processing offers interesting possibilities of performer-instrument interaction for musical expression. These new musical instruments can precisely modulate the haptic feedback, and map it to a sonic output, thus ...

The aim of the study is to transpose and extend to a set of environmental sounds the notion of sound descriptors usually used for musical sounds. Four separate primary studies dealing with interior car sounds, air-conditioning units, car horns, and closing car doors are considered collectively. The corpus formed by these initial stimuli is submitted to new experimental studies and analyses, both for revealing metacategories and for defining more precisely the limits of each of the resulting categories. In a second step, the new structure is modeled: common and specific dimensions within each category are derived from the initial results and new investigations of audio features are performed. Furthermore, an automatic classifier based on two audio descriptors and a multinomial logistic regression procedure is implemented and validated with the corpus.

The aim of this paper is to improve beat-tracking for live guitar performances. Beat-tracking is a function to estimate musical measurements, for example musical tempo and phase. This method is critical to achieve a synchronized ensemble performance such as musical robot accompaniment. Beat-tracking of a live guitar performance has to deal with three challenges: tempo fluctuation, beat pattern complexity and environmental noise. To cope with these problems, we devise an audiovisual integration method for beat-tracking. The auditory beat features are estimated in terms of tactus (phase) and tempo (period) by Spectro-Temporal Pattern Matching (STPM), robust against stationary noise. The visual beat features are estimated by tracking the position of the hand relative to the guitar using optical flow, mean shift and the Hough transform. Both estimated features are integrated using a particle filter to aggregate the multimodal information based on a beat location model and a hand's trajectory model. Experimental results confirm that our beat-tracking improves the F-measure by 8.9 points on average over the Murata beat-tracking method, which uses STPM and rule-based beat detection. The results also show that the system is capable of real-time processing with a suppressed number of particles while preserving the estimation accuracy. We demonstrate an ensemble with the humanoid HRP-2 that plays the theremin with a human guitarist.