Fuzzy System for the Classification of Sounds of Birds Based on the Audio Descriptors

Tyburek, Krzysztof; Prokopowicz, Piotr; Kotlarz, Piotr

doi:10.1007/978-3-319-07176-3_61

Cited by 5 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are some physical features applied for indexing speech, like: spectrum irregularity, wide and narrow band spectrograms, speech signals filtering and processing, enhancement and manipulation of specific frequency regions, segmentation and labeling of words, syllables and individual phonemes [37]. Moreover, the Mel-Frequency Cepstral Coefficients (MFCC) is widely used in speech classification experiments [39]. For the reduction of leakage effect, the Hamming window is implemented.…”

Section: Wael Badawymentioning

confidence: 99%

BEET: Behavior Engagement Emotion Trigger – A AI-based model to maximize audience engagement

Badawy

2021

Menoufia Journal of Electronic Engineering Research

View full text Add to dashboard Cite

Engagement and measuring is a key performance for any content. The wide spread of the Internet and the adoption in our daily life, audience has many options to explore and audience developed the power of rejection. This power of rejection reduces the time of exposure and engagement to any contents including the in person engagement. The access to the online tools and portals provide an escape route from human-tohuman interaction. A new tool to maximize audience engagement to a content that can influence a decision becomes a key performance and an asset for exposure. It enables a better delivery of the Contents, an increased interaction with the content, simplify the process of creating an engaging content, and maximize the exposure time. This paper presents a prototype, and evaluation of the performance of an automated Content Behavior Emotion Engagement Model "BEEM" that uses Deep Learning and Big Data analysis to discover the Behavior Emotion trend of the audience. BEETAIR is a new innovative framework that will transform the media market through Behavior Emotion Engagement Trigger Analyzer, and Intelligent Recommender. It uses The Artificial Intelligent-based Dialogue Generator for Maximum Audience Engagement.

show abstract

Section: Wael Badawymentioning

confidence: 99%

BEET: Behavior Engagement Emotion Trigger – A AI-based model to maximize audience engagement

Badawy

2021

Menoufia Journal of Electronic Engineering Research

View full text Add to dashboard Cite

show abstract

“…To calculate these coefficients the inverse cosine transform of decimal logarithm of the Mel filtered short-term spectrum of energy must be done. The purpose of improving results of experiment and searching effective spectral feature vector, constant spectral resolution is used [39]. For the reduction of leakage effect, the Hamming window is implemented.…”

Section: Feature Extractionmentioning

confidence: 99%

Survey on AI-Based Multimodal Methods for Emotion Detection

Maréchal

Mikołajewski

Tyburek

et al. 2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Automatic emotion recognition constitutes one of the great challenges providing new tools for more objective and quicker diagnosis, communication and research. Quick and accurate emotion recognition may increase possibilities of computers, robots, and integrated environments to recognize human emotions, and response accordingly to them a social rules. The purpose of this paper is to investigate the possibility of automated emotion representation, recognition and prediction its state-of-the-art and main directions for further research. We focus on the impact of emotion analysis and state of the arts of multimodal emotion detection. We present existing works, possibilities and existing methods to analyze emotion in text, sound, image, video and physiological signals. We also emphasize the most important features for all available emotion recognition modes. Finally, we present the available platform and outlines the existing projects, which deal with multimodal emotion analysis.

show abstract

“…Although the research described in this publication relate to other descriptors and other birds species, the idea of a classifier is based on the concept presented in (Tyburek et al,2014) as proposition 2.…”

Section: Linguistic Modelling Of Fuzzy Classifiermentioning

confidence: 99%

Computational Intelligence in a Classification of Audio Recordings of Nature

Tyburek

Prokopowicz

Kotlarz³

2014

Proceedings of the International Conference on Fuzzy Computation Theory and Applications

Self Cite

View full text Add to dashboard Cite

This paper presents different ways for a classification of sounds of birds using linguistic approach with a fuzzy system, neural network and WEKA system. Features of sounds of birds species are coded by the selected MPEG-7 descriptors. The models of classification system are based on the audio descriptors for a some chosen species of birds like: Corn Crake, Hawk, Blackbird, Cuckoo, Lesser Whitethroat, Chiffchaff, Eurasian Pygmy Owl, Meadow Pipit, House Sparrow, Firecrest. The paper proposes fuzzy models that definitely bases on the linguistic description. Moreover neural network for classification was proposed. As reference results WEKA system is used.

show abstract

Fuzzy System for the Classification of Sounds of Birds Based on the Audio Descriptors

Cited by 5 publications

References 4 publications

BEET: Behavior Engagement Emotion Trigger – A AI-based model to maximize audience engagement

BEET: Behavior Engagement Emotion Trigger – A AI-based model to maximize audience engagement

Survey on AI-Based Multimodal Methods for Emotion Detection

Computational Intelligence in a Classification of Audio Recordings of Nature

Contact Info

Product

Resources

About