Optimizing the length of an environmental audio fingerprint for place classification

@article{DelgadoContreras2016OptimizingTL,
  title={Optimizing the length of an environmental audio fingerprint for place classification},
  author={J. Ruben Delgado-Contreras and Juan-Pablo Garc{\'i}a-V{\'a}zquez and Ram{\'o}n F. Brena},
  journal={2016 International Conference on Electronics, Communications and Computers (CONIELECOMP)},
  year={2016},
  pages={106-112}
}
One of the many possible sources for identifying a place is environmental sound. Ambient sound can be used by itself or in combination with other methods, like GPS, WiFi, etc. A way of identifying a place with sound is using "fingerprinting", which tries to match features of sound in similar places with the one being registered. Nevertheless, one of the many parameters in this process relates to the length of the audio both for the patterns and for the current recording. Several authors use a… 

Tables from this paper

Potential Barriers to Music Fingerprinting Algorithms in the Presence of Background Noise
TLDR
A comprehensive and powerful survey of already developed algorithms for music fingerprinting algorithms is conducted and shows that the results of music fingerprint classification are more successful when deep learning techniques for classification are used.
A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks
TLDR
A novel approach for the HAR based on acoustic data and similarity networks is proposed, able to characterize the sound of the activities and identify those activities looking for similarity in the sound pattern.

References

SHOWING 1-10 OF 19 REFERENCES
Classification of environmental audio signals using statistical time and frequency features
TLDR
An approach for location classification that does not need to have an explicit information about the place, in contrast with systems such as a Quick Response Code (QR) or Radio Frequency Identificator tag is presented.
Robust features for environmental sound classification
  • S. Sivasankaran, K. Prabhu
  • Computer Science
    2013 IEEE International Conference on Electronics, Computing and Communication Technologies
  • 2013
TLDR
Algorithm to classify environmental sounds with the aim of providing contextual information to devices such as hearing aids for optimum performance using signal sub-band energy and Gaussian mixture model is described.
Classification of audio signals using statistical features on time and wavelet transform domains
TLDR
The preliminary results suggest that the features collected by the adaptive splitting wavelet transform technique performed better compared to the other wavelet based techniques, achieving an overall classification accuracy of 91.67%, using either the minimum distance classifier or the least squares minimum distanceclassifier.
A Review of Audio Fingerprinting
TLDR
Different techniques describing its functional blocks as parts of a common, unified framework for audio fingerprinting are reviewed.
Audio content analysis for online audiovisual data segmentation and classification
TLDR
A heuristic rule-based procedure is proposed to segment and classify audio signals and built upon morphological and statistical analysis of the time-varying functions of these audio features.
Environmental Sound Classification using Hybrid SVM/KNN Classifier and MPEG-7 Audio Low-Level Descriptor
TLDR
The performance comparison between the HMM sound classifier using audio spectrum projection features demonstrates the superiority of the proposed scheme.
Construction and evaluation of a robust multifeature speech/music discriminator
  • E. D. Scheirer, M. Slaney
  • Computer Science
    1997 IEEE International Conference on Acoustics, Speech, and Signal Processing
  • 1997
TLDR
A real-time computer system capable of distinguishing speech signals from music signals over a wide range of digital audio input is constructed and extensive data on system performance and the cross-validated training/test setup used to evaluate the system is provided.
Situational Awareness from Environmental Sounds
TLDR
This work will present the different phases of the project: capture of environmental audio, pre-processing of audio data, feature extraction using power spectral density and filter-banks, training and testing of a simple nearest-neighbor algorithm, and some preliminary results and future work on incorporating such techniques on a wearable computer to provide a form of "situational awareness" to the system.
Content Fingerprinting Using Wavelets
TLDR
Waveprint uses a combination of computer-vision techniques and large-scale-data-stream processing algorithms to create compact fingerprints of audio data that can be efficiently matched, and explicitly measures the tradeoffs between performance, memory usage, and computation.
Survey and Evaluation of Audio Fingerprinting Schemes for Mobile Query-by-Example Applications
We survey and evaluate popular audio fingerprinting schemes in a common framework with short query probes captured from cell phones. We report and discuss results important for mobile applications:
...
1
2
...