Metrics for Polyphonic Sound Event Detection

@article{Mesaros2016MetricsFP,
  title={Metrics for Polyphonic Sound Event Detection},
  author={Annamaria Mesaros and Toni Heittola and Tuomas Virtanen},
  journal={Applied Sciences},
  year={2016},
  volume={6},
  pages={162}
}
This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously. The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time. The polyphonic system output requires a suitable procedure for evaluation against a reference. Metrics from neighboring fields such as speech… 
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References

SHOWING 1-10 OF 52 REFERENCES
Acoustic event detection for multiple overlapping similar sources
  • D. Stowell, David Clayton
  • Physics
    2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
  • 2015
TLDR
A simple method modelling the onsets, durations and offsets of acoustic events to avoid intrinsic limits on polyphony or on inter-event temporal patterns is introduced and evaluated in a case study with over 3000 zebra finch calls.
Context-dependent sound event detection
TLDR
The two-step approach was found to improve the results substantially compared to the context-independent baseline system, and the detection accuracy can be almost doubled by using the proposed context-dependent event detection.
Acoustic event detection in real life recordings
TLDR
A system for acoustic event detection in recordings from real life environments using a network of hidden Markov models, capable of recognizing almost one third of the events, and the temporal positioning of the Events is not correct for 84% of the time.
Events Detection for an Audio-Based Surveillance System
TLDR
The automatic shot detection system presented is based on a novelty detection approach which offers a solution to detect abnormality (abnormal audio events) in continuous audio recordings of public places and takes advantage of potential similarity between the acoustic signatures of the different types of weapons by building a hierarchical classification system.
Reliable detection of audio events in highly noisy environments
Polyphonic sound event detection using multi label deep neural networks
TLDR
Frame-wise spectral-domain features are used as inputs to train a deep neural network for multi label classification in this work and the proposed method improves the accuracy by 19% percentage points overall.
Supervised model training for overlapping sound events based on unsupervised source separation
TLDR
Two iterative approaches based on EM algorithm to select the most likely stream to contain the target sound to give a reasonable increase of 8 percentage units in the detection accuracy are proposed.
Real-world acoustic event detection
Acoustic Event Detection and Classification
The human activity that takes place in meeting rooms or classrooms is reflected in a rich variety of acoustic events (AE), produced either by the human body or by objects handled by humans, so the…
Sound Event Recognition With Probabilistic Distance SVMs
  • H. D. Tran, Haizhou Li
  • Computer Science
    IEEE Transactions on Audio, Speech, and Language Processing
  • 2011
TLDR
The results show that the proposed classification method significantly outperforms conventional SVM classifiers with Mel-frequency cepstral coefficients (MFCCs) and makes the proposed method an obvious choice for online sound event recognition.
...
...