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What Is The Effect Of Audio Quality On The Robustness Of Mfccs And Chroma Features?

Julián Urbano, Dmitry Bogdanov, Perfecto Herrera, Emilia Gómez, Xavier Serra
2014 Zenodo  
Gómez and X. Serra. "What is the Effect of Audio Quality on the Robustness of MFCCs and Chroma Features?", 15th International Society for Music Information Retrieval Conference, 2014.  ...  As they pervade the literature on MIR, we analyzed the effect of audio encoding and signal analysis parameters on the robustness of MFCCs and chroma.  ... 
doi:10.5281/zenodo.1416276 fatcat:k4junbbvpnfyncdjwe6qvdjngm

Listening to features [article]

Manuel Moussallam and Antoine Liutkus and Laurent Daudet
2015 arXiv   pre-print
Whereas some previous studies already considered the problem of synthesizing audio from features such as Mel-Frequency Cepstral Coefficients, they mainly relied on the explicit formula used to compute  ...  What makes this task harder is twofold. First, that features are irregularly spaced in the temporal domain according to an onset-based segmentation.  ...  Timbre coefficients seem more robust to the task than Chroma and Loudness ones.  ... 
arXiv:1501.04981v1 fatcat:jkssfo6eofaehmwwv3lkrtizfa

An Analysis Of Chorus Features In Popular Song

Jan Van Balen, John Ashley Burgoyne, Frans Wiering, Remco C. Veltkamp
2013 Zenodo  
This effect is observed for Chroma Variance and Pitch Centroid IQR, though not for MFCC Variance and Loudness IQR.  ...  Chroma Variance Chroma features are widely used to capture harmony and harmonic changes.  ... 
doi:10.5281/zenodo.1415623 fatcat:eqz2luywhrfu3bi2fkk4saga2m

Features for Content-Based Audio Retrieval [chapter]

Dalibor Mitrović, Matthias Zeppelzauer, Christian Breiteneder
2010 Advances in Computers  
The goal of this paper is to review latest research in the context of audio feature extraction and to give an application-independent overview of the most important existing techniques.  ...  We survey state-of-the-art features from various domains and propose a novel taxonomy for the organization of audio features.  ...  This work has received financial support from the Vienna Science and Technology Fund (WWTF) under grant no. CI06 024.  ... 
doi:10.1016/s0065-2458(10)78003-7 fatcat:u3re7clvgvhivi4oqnfkh6xiiu

Surfboard: Audio Feature Extraction for Modern Machine Learning [article]

Raphael Lenain, Jack Weston, Abhishek Shivkumar, Emil Fristed
2020 arXiv   pre-print
It builds on state-of-the-art audio analysis packages and offers multiprocessing support for processing large workloads.  ...  Surfboard is written with the aim of addressing pain points of existing libraries and facilitating joint use with modern machine learning frameworks.  ...  Surfboard is a Python package for audio feature extraction, written with the aim of making a library better suited to fast prototyping and modern machine learning (ML) applications than what is offered  ... 
arXiv:2005.08848v1 fatcat:yvokvtbyjzgdxnw4b2tkx37bvu

Surfboard: Audio Feature Extraction for Modern Machine Learning

Raphael Lenain, Jack Weston, Abhishek Shivkumar, Emil Fristed
2020 Interspeech 2020  
It builds on state-of-the-art audio analysis packages and offers multiprocessing support for processing large workloads.  ...  Surfboard is written with the aim of addressing pain points of existing libraries and facilitating joint use with modern machine learning frameworks.  ...  Surfboard is a Python package for audio feature extraction, written with the aim of making a library better suited to fast prototyping and modern machine learning (ML) applications than what is offered  ... 
doi:10.21437/interspeech.2020-2879 dblp:conf/interspeech/LenainWSF20 fatcat:lrmasduitjetdharuj5qskqufe

A Music Classification Method Based On Timbral Features

Thibault Langlois, Gonçalo Marques
2009 Zenodo  
One of the benefits of our method is that once the models are computed, there is no need to have access to the audio files and MFCC features since only the sequences of symbols are used.  ...  The results shown in rows 3 and 4 are worse than those obtained by Dan Ellis [15] since his approach leads to 54% accuracy using MFCC features and 57% using MFCC and chroma features. k As we can see,  ... 
doi:10.5281/zenodo.1417863 fatcat:n3tshbaacffsvhafj3agim46ze

Using quadratic programming to estimate feature relevance in structural analyses of music

Jordan B.L. Smith, Elaine Chew
2013 Proceedings of the 21st ACM international conference on Multimedia - MM '13  
(QP) to minimize the distance between a linear sum of these components and the annotated description.  ...  We posit that the optimal section-wise weights on the feature components may indicate the features to which a listener attended when annotating a piece, and thus may help us to understand why two listeners  ...  This research was supported in part by the Social Sciences and Humanities Research Council of Canada and by a QMUL EPSRC Doctoral Training Account studentship.  ... 
doi:10.1145/2502081.2502124 dblp:conf/mm/SmithC13 fatcat:ti6tbnzzh5epvdqywyt77yipju

Codebook-Based Audio Feature Representation for Music Information Retrieval

Yonatan Vaizman, Brian McFee, Gert Lanckriet
2014 IEEE/ACM Transactions on Audio Speech and Language Processing  
Before designing new audio features, we explore the usage of traditional local features, while adding a stage of encoding with a pre-computed codebook and a stage of pooling to get compact vectorial representations  ...  When manual annotations and user preference data is lacking (e.g. for new artists) these systems must rely on content based methods.  ...  While MFCC is considered as capturing timbral qualities of the sound, the CQT and chroma features are designed for harmonic properties of the music (or melodic, if using patches of multiple frames).  ... 
doi:10.1109/taslp.2014.2337842 fatcat:am4u4pxwfbgcvonfbfbc5byeom

Wind Sounds Classification Using Different Audio Feature Extraction Techniques

Wala'a Nsaif Jasim, Saba Abdual Wahid Saddam, Esra'a Jasem Harfash
2022 Informatica (Ljubljana, Tiskana izd.)  
The CNN classification method is implemented to determine the class of input wind sound signal.  ...  In this research, different audio feature extraction techniques are implemented and classification approaches are presented to classify seven types of wind.  ...  In their proposed system Log-mel spectrogram, chroma, spectral contrast and tonnetz are aggregated to compose the feature sets of LMC, and MFCC is jointed with spectral contrast, chroma and tonnetz to  ... 
doi:10.31449/inf.v45i7.3739 fatcat:di3qc3tltnejzfdodlribmj4cq

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

Francesc Alías, Joan Socoró, Xavier Sevillano
2016 Applied Sciences  
In this paper, we present an up-to-date review of the most relevant audio feature extraction techniques developed to analyze the most usual audio signals: speech, music and environmental sounds.  ...  To achieve this ambitious aim, the representation of the audio signal is of paramount importance.  ...  pleasantness and the quality of noises.  ... 
doi:10.3390/app6050143 fatcat:y5lptonferfatbljlz2wixhppi

Feature learning and deep architectures: new directions for music informatics

Eric J. Humphrey, Juan P. Bello, Yann LeCun
2013 Journal of Intelligent Information Systems  
On closer inspection, performance trajectories across several applications reveal that this is indeed the case, raising some difficult questions for the discipline: why are we slowing down, and what can  ...  Through conceptual arguments for feature learning and deeper processing architectures, we demonstrate how deep processing models are more powerful extensions of current methods, and why now is the time  ...  a few simpler operations; on closer inspection, the main difference between chroma and MFCCs is the parameters used. remains, by and large, a manual process.  ... 
doi:10.1007/s10844-013-0248-5 fatcat:m6n4kfas7nbtvk6cja6hbgzihq

Emotion Recognition from Speech [article]

Kannan Venkataramanan, Haresh Rengaraj Rajamohan
2019 arXiv   pre-print
The analyses were carried out on audio recordings from Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS).  ...  After pre-processing the raw audio files, features such as Log-Mel Spectrogram, Mel-Frequency Cepstral Coefficients (MFCCs), pitch and energy were considered.  ...  Chroma Features Chroma features are representation for audio in which the entire spectrum is projected onto 12 bins representing the 12 distinct semitones (or chroma) of the musical octave [15] .  ... 
arXiv:1912.10458v1 fatcat:jxl2uwpebfeatdqaao4duyqp6i

DGR: Gender Recognition of Human Speech Using One-Dimensional Conventional Neural Network

Rami S. Alkhawaldeh
2019 Scientific Programming  
Experimentation reveals that the best recall value is equal to 99.97%; the best recall value is 99.7% for two models of deep learning (DL) and support vector machine (SVM), and with feature selection,  ...  Gender voice is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications.  ...  models. e robustness and effectiveness of classifiers are determined by the quality of features that depend on a training set employing machine learning (ML) techniques. erefore, eliciting voice features  ... 
doi:10.1155/2019/7213717 fatcat:hujorwwcg5cejeyu275eu6az2e

Supervised machine learning for audio emotion recognition

Stuart Cunningham, Harrison Ridley, Jonathan Weinel, Richard Picking
2020 Personal and Ubiquitous Computing  
The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson's r correlation coefficient before being used as inputs  ...  It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than  ...  standard deviation of MFCCs 1, 2, 8 and 10; and the standard deviation of chroma vector 10, p < 0.05.  ... 
doi:10.1007/s00779-020-01389-0 fatcat:m7wbyavacvdwdmthlfccqr2gde
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