Development of a Reference Platform for Generic Audio Classification

Jarina, Roman; Paralic, Martin; Kuba, Michal; Olajec, J.; Lukac, A.; Dzurek, M.

doi:10.1109/wiamis.2008.39

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Experiments with elementary sound models were performed on AudioDat sound database [11], which include following sound classes: Applause (crowd), Crying, Laughing (crowd), Music, Noise (nature and industrial environmental sounds) and Speech (different languages and genders). All audio files are 16 bits PCM samples with 22050 Hz sampling frequency.…”

Section: A Databasementioning

confidence: 99%

A comparison of audio features for elementary sound based audio classification

Gubka

Kuba

2013

The International Conference on Digital Technologies 2013

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In this paper we compare two sets of audio features in task of audio pattern searching based on elementary sound models. The rst set of features consist of well-known melfrequency cepstral coef cients together with their rst and second order time derivatives. The second set was chosen from bag of features by particle swarm optimization algorithm and consist of following audio features: line spectral frequencies (LSF), spectral ux (SFX) and zero crossing rate (ZCR). Experimental results performed on AudioDat sound database show improvement of above 18.6 % of average F-measure when using the second selected combination of features.

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Section: A Databasementioning

confidence: 99%

A comparison of audio features for elementary sound based audio classification

Gubka

Kuba

2013

The International Conference on Digital Technologies 2013

Self Cite

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“…Experiments with elementary sound models were performed on AudioDat sound database [13], which include following sound classes: Applause (crowd), Crying, Laughing (crowd), Music, Noise (nature and industrial environment sounds) and Speech (different languages and genders). More about the database size shows the Table I.…”

Section: A Databasementioning

confidence: 99%

Elementary sound based audio pattern searching

Gubka

Kuba

2013

2013 23rd International Conference Radioelektronika (RADIOELEKTRONIKA)

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In the paper, we proposed a novel approach for query-by-example audio patterns searching method inspired by classical phoneme-based word spotting and speech recognition systems, where larger and more complex models are created as a concatenation of pre-trained phoneme sub-models. Unlike most other methods for sound events classification which uses pretrained sound classes, our system has no default limitations for demanded sound event and any sound example can be added into the search space. Using methods of cluster analysis and Viterbi alignment, we created a database of what we call "elementary sound"models. These models serves as elementary units of sound event and gives our system flexibility and versatility in adding new patterns into query. In the paper, the methodology of elementary sound models database creation and its characteristics are presented. Also process of creating and embedding models of new patterns into the model network is described.

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Large-scale audio dataset for emergency vehicle sirens and road noises

et al. 2022

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Traffic congestion, accidents, and pollution are becoming a challenge for researchers. It is essential to develop new ideas to solve these problems, either by improving the infrastructure or applying the latest technology to use the existing infrastructure better. This research paper presents a high-resolution dataset that will help the research community to apply AI techniques to classify any emergency vehicle from traffic and road noises. Demand for such datasets is high as they can control traffic flow and reduce traffic congestion. It also improves emergency response time, especially for fire and health events. This work collects audio data using different methods, and pre-processed them to develop a high-quality and clean dataset. The dataset is divided into two labelled classes one for emergency vehicle sirens and one for traffic noises. The developed dataset offers high quality and range of real-world traffic sounds and emergency vehicle sirens. The technical validity of the dataset is also established.

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Development of a Reference Platform for Generic Audio Classification

Cited by 5 publications

References 12 publications

A comparison of audio features for elementary sound based audio classification

A comparison of audio features for elementary sound based audio classification

Elementary sound based audio pattern searching

Large-scale audio dataset for emergency vehicle sirens and road noises

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