Parametric Representations of Bird Sounds for Automatic Species Recognition

Somervuo, Panu; Härmä, Aki; Fagerlund, Seppo

doi:10.1109/tasl.2006.872624

Cited by 176 publications

(158 citation statements)

References 24 publications

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“…Panu Somervuo, Aki Harma, and Seppo Fagerlund [10] segmented a recording into individual syllables using an iterative time-domain algorithm and then parameterized each segmented region using three models such as Sinusoidal Model, Mel-Cepstrum Model, and Descriptive Parameters. Dynamic time warping (DTW) algorithm was used for comparing variable length sequences.…”

Section: Related Workmentioning

confidence: 99%

Audio based Bird Species Recognition Using Naïve Bayes Algorithm

2018

IJMTER

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Abstract-Small of the smallest living creature is the part of the whole ecosystem. Monitoring and maintaining this environmental health has become a task of prime importance, since human activity and climate change has placed the greater pressure on biodiversity. This helps in monitoring dangerous situations to planes caused by birds near airports and also in saving endangered species. Powerful audio signal processing techniques makes it possible to introduce automated methods for the detection of such vocalizations. The complete identification procedure essentially involves collection of recorded vocalizations of different species, audio pre-processing to remove the noise and silence, segmentation to select the most representative characterizing elements of the signal called syllable, extraction of different features commonly found in sound classification and speech recognition to obtain relevant characteristics, and decision procedures for identification, where Machine Learning (ML) algorithms are used to train employed classifiers using labelled database of previously known species of interest. Naïve Bayes algorithm is used for classification and hence recognition of bird species. The required class probabilities are found to be as good as 100% and as worst as 97% in some cases.

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Section: Related Workmentioning

confidence: 99%

Audio based Bird Species Recognition Using Naïve Bayes Algorithm

2018

IJMTER

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“…Experiments were conducted in order to evaluate various combinations of feature sets and classifiers in a database composed by 101 audio records from 3 bird species. Somervuo et al [10] found that MFCC outperforms sinusoidal features and a collection of spectral features such as spectral centroid, bandwidth, roll-off, flux, etc. Lee etc.…”

Section: Introductionmentioning

confidence: 99%

A Comparison Study to Identify Birds Species Based on Bird Song Signals

Guo

2017

ITM Web Conf.

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this paper presents a comparison study in automatically identifying bird species based on bird acoustic signals, using audio files from XENO-CANTO online database. The features including Mel-Frequency Cepstral Coefficients (MFCC), geo-related meta-features, and the integration are compared. The learning classifiers Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Ensemble Learning are examined. Our experimental results show that in the comparison study, ensemble learning using discriminant learner with the integration of MFCC features and geo meta-features obtains the best detection performance.

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“…Typically, the first stage of an automatic system is to parse the acoustic signal into isolated spectro-temporal segments. This is often performed using an energy-based thresholding that requires an estimate of noise level, e.g., [1], or by decomposition into sinusoidal components [1], [2], [3], [4]. A variety of approaches to feature representation of the spectro-temporal segments and their modelling were explored.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of bird species from audio field recordings using HMM-based modelling of frequency tracks

Jančovič

Köküer

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper presents an automatic system for detection of bird species in field recordings. A sinusoidal detection algorithm is employed to segment the acoustic scene into isolated spectro-temporal segments. Each segment is represented as a temporal sequence of frequencies of the detected sinusoid, referred to as frequency track. Each bird species is represented by a set of hidden Markov models (HMMs), each HMM modelling an individual type of bird vocalisation element. These HMMs are obtained in an unsupervised manner. The detection is based on a likelihood ratio of the test utterance against the target bird species and non-target background model. We explore on selection of cohort for modelling the background model, z-norm and t-norm score normalisation techniques and score compensation to deal with outlier data. Experiments are performed using over 40 hours of audio field recordings from 48 bird species plus an additional 16 hours of field recordings as impostor trials. Evaluations are performed using detection error trade-off plots. The equal error rate of 5% is achieved when impostor trials are non-target bird species vocalisations and 1.2% when using field recordings which do not contain bird vocalisations.

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Parametric Representations of Bird Sounds for Automatic Species Recognition

Cited by 176 publications

References 24 publications

Audio based Bird Species Recognition Using Naïve Bayes Algorithm

Audio based Bird Species Recognition Using Naïve Bayes Algorithm

A Comparison Study to Identify Birds Species Based on Bird Song Signals

Automatic detection of bird species from audio field recordings using HMM-based modelling of frequency tracks

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