The thirteen colors of timbre

Terasawa, Hiroko; Slaney, Malcolm; Berger, Jonah

doi:10.1109/aspaa.2005.1540234

Cited by 34 publications

(25 citation statements)

References 5 publications

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“…This scale, called mel, is the basis for the mel-frequency cepstral coefficients (MFCC). MFCCs are popular features in various application domains, particularly speech and speaker recognition [43] as well as musical instruments classification [36]. Equation 12 defines the scale conversion from frequency (f ) to mel (m).…”

Section: Feature Extraction In Cepstral Representationsmentioning

confidence: 99%

Exploring Low Cost Laser Sensors to Identify Flying Insect Species

Silva

Souza

Ellis

et al. 2015

J Intell Robot Syst

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Insects have a close relationship with the humanity, in both positive and negative ways. Mosquito borne diseases kill millions of people and insect pests consume and destroy around US $40 billion worth of food each year. In contrast, insects pollinate at least two-thirds of all the food consumed in the world. In order to control populations of disease vectors and agricultural pests, researchers in entomology have developed numerous methods including chemical, biological and mechanical approaches. However, without the knowledge of the exact location of the insects, the use of these techniques becomes costly and inefficient. We are developing a novel sensor as a tool to control disease vectors and agricultural pests. This sensor, which is built from inexpensive commodity electronics, captures insect flight information using laser light and classifies the insects according to their species. The use of machine learning techniques allows the sensor to automatically identify the species without human intervention. Finally, the sensor can provide real-time estimates of insect species with virtually no time gap between the insect identification and the delivery of population estimates. In this paper, we present our solution to the most important challenge to make this sensor practical: the creation of an accurate classification system. We show that, with the correct combination of feature extraction and machine learning techniques, we can achieve an accuracy of almost 90 % in the task of identifying the correct insect species among nine species. Specifically, we show that we can achieve an accuracy of 95 % in the task of correctly recognizing if a given event was generated by a disease vector mosquito.

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Section: Feature Extraction In Cepstral Representationsmentioning

confidence: 99%

Exploring Low Cost Laser Sensors to Identify Flying Insect Species

Silva

Souza

Ellis

et al. 2015

J Intell Robot Syst

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“…Since there are literally hundreds of signal processing features available in the literature, we performed several experiments to investigate which features would provide the best results for our domain. We experimented with features used in similar applications such as spoken digit recognition [14], music instrument recognition [15] and recognition of species of animals by their calls [16]. Due to lack of space, we will reserve these results for a future publication.…”

Section: Laser Insect Sensormentioning

confidence: 99%

Classification of Data Streams Applied to Insect Recognition: Initial Results

Souza¹,

Silva²,

Batista³

2013

2013 Brazilian Conference on Intelligent Systems

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Abstract-Applications such as intelligent sensors should be able to collect information about the environment and make decisions based on input data. An example is a lowcost sensor able to detect and classify species of insects using a simple laser and machine learning techniques. This sensor is an important step towards the development of intelligent traps able to attract and selectively capture insect species of interest such as disease vectors or agricultural pests, without affecting non-harmful species. The data gathered by the sensor constitutes a data stream with non-stationary characteristics, since the insects' metabolisms are influenced by environmental conditions (such as temperature, humidity and atmospheric pressure), circadian rhythm and age. Algorithms that classify data streams often assume that once a prediction is made, the actual labels are provided to assist in updating the classifier. In the case of intelligent sensors, these labels are rarely available. The objective of this paper is to evaluate methods that adapt concept drifts by regularly updating the classification models applied to insect recognition in a data stream. We show in our initial results that the philosophy of inserting and removing examples from the training set are of essential importance. We also show that a simple criterion to insert examples with high classification confidence can significantly improve the accuracy.

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“…Mel-frequency cepstral coefficients (MFCC) are often chosen as a metric for spectral envelope perception because of their linearity, orthogonality, and multidimensionality (Terasawa et al, 2012). They were also applied in a study by Rump et al (2007), which aimed at the improvement of accuracy of MFCCbased genre classification by applying the HarmonicPercussion Signal Separation (HPSS) algorithm to the music signal, and then calculating the MFCCs on the separated signals.…”

Section: Drum Separation Algorithmmentioning

confidence: 99%

Classification of Music Genres Based on Music Separation into Harmonic and Drum Components

Rosner¹,

Schuller²,

Kostek³

2015

Archives of Acoustics

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This article presents a study on music genre classification based on music separation into harmonic and drum components. For this purpose, audio signal separation is executed to extend the overall vector of parameters by new descriptors extracted from harmonic and/or drum music content. The study is performed using the ISMIS database of music files represented by vectors of parameters containing music features. The Support Vector Machine (SVM) classifier and co-training method adapted for the standard SVM are involved in genre classification. Also, some additional experiments are performed using reduced feature vectors, which improved the overall result. Finally, results and conclusions drawn from the study are presented, and suggestions for further work are outlined.

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The thirteen colors of timbre

Cited by 34 publications

References 5 publications

Exploring Low Cost Laser Sensors to Identify Flying Insect Species

Exploring Low Cost Laser Sensors to Identify Flying Insect Species

Classification of Data Streams Applied to Insect Recognition: Initial Results

Classification of Music Genres Based on Music Separation into Harmonic and Drum Components

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