Abstract
Monitoring techniques are a key technology for examining the conditions in various scenarios, e.g., structural conditions, weather conditions, and disasters. In order to understand such scenarios, the appropriate extraction of their features from observation data is important. This paper proposes a monitoring method that allows sound environments to be expressed as a sound pattern. To this end, the concept of synesthesia is exploited. That is, the keys, tones, and pitches of the monitored sound are expressed using the three elements of color, that is, the hue, saturation, and brightness, respectively. In this paper, it is assumed that the hue, saturation, and brightness can be detected from the chromagram, sonogram, and sound spectrogram, respectively, based on a previous synesthesia experiment. Then, the sound pattern can be drawn using color, yielding a “painted sound map.” The usefulness of the proposed monitoring technique is verified using environmental sound data observed at a galleria.
Highlights
The analysis of large data sets, so-called “big data,” has allowed a variety of information to be extracted, and this information can help create certain services
This paper proposes a monitoring method that allows sound environments to be expressed as a sound pattern
environmental sound recognition (ESR) techniques implemented with features such as a zero-crossing rate, Cepstral features, MPEG-7based features, and autoregression-based features, which are extracted from environmental sounds, have been proposed [3]-[9], along with a method of understanding environmental sounds that employs a matching pursuit algorithm [10]
Summary
The analysis of large data sets, so-called “big data,” has allowed a variety of information to be extracted, and this information can help create certain services. Monitoring techniques are regarded as those that allow identification of the monitored environment conditions through analysis of the data observed within the area. Various methods for understanding sound environments have been proposed to date. ESR techniques implemented with features such as a zero-crossing rate, Cepstral features, MPEG-7based features, and autoregression-based features, which are extracted from environmental sounds, have been proposed [3]-[9], along with a method of understanding environmental sounds that employs a matching pursuit algorithm [10]. This study proposes an unconventional method that allows the analysis of sound environments using color, where the color rules are based on the concept of synesthesia [11]. The efficacy of the proposed monitoring method is evaluated using environmental sound data observed at a galleria
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