Musical note onset detection is a building component for several MIR related tasks. The ambiguity in the definition of a note onset and the lack of a standard way to annotate onsets, introduce differences in datasets ...
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Musical note onset detection is a building component for several MIR related tasks. The ambiguity in the definition of a note onset and the lack of a standard way to annotate onsets, introduce differences in datasets labeling, which in turn makes evaluations of note onset detection algorithms difficult to compare. This paper gives an overview of the parameters influencing the commonly used onset detection evaluation measure, i.e. the F1-score, pointing out a consistently missing parameter which is the overall time shift in annotations. This paper shows how crucial this parameter is in making reported F1-scores comparable among different algorithms and datasets, achieving a more reliable evaluation. As several MIR applications are concerned with the relative location of onsets to each other and not their absolute location, this paper suggests to include the overall time shift as a parameter when evaluating the algorithm performance. Experiments show a strong variability in the reported F1-score and up to 50% increase in the best-case F1-score when varying the overall time shift. Optimizing the time shift turns out to be crucial when training or testing algorithms with datasets that are annotated differently (e.g. manually, automatically, and with different annotators) and especially when using deep learning algorithms.
The previous approaches have failed to effectually score the language proficiency of a non-native speakers especially in case of non- English languages which are complex and a slight change of pronunciation can alter ...
ISBN:
(数字)9781728145815
ISBN:
(纸本)9781728145822
The previous approaches have failed to effectually score the language proficiency of a non-native speakers especially in case of non- English languages which are complex and a slight change of pronunciation can alter the nature of the word. In this study, we proposed an automated language scoring system to test the proficiency of Chinese language. We have employed a novel fusion approach of a 38-feature based model and a Siamese convolutional neural network (Siamese CNN) which can accuracy identify the difference between the native speech and the test taker's speech. The results show that out model have achieved comparable performance to the state of the art and solved the pronunciation problems as well. Furthermore, we have provided a fusion based approach and provided extensive amount of experiments which shows that our method is state of the art and can be utilized in real time Chinese language proficiency scoring.
Hospital systems routinely assign disease codes (ICD10 codes) to medical records. The challenge stands on treating natural and nonstandard language in which doctors express their diagnoses and, additionally, to solve ...
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Hospital systems routinely assign disease codes (ICD10 codes) to medical records. The challenge stands on treating natural and nonstandard language in which doctors express their diagnoses and, additionally, to solve a large-scale classification problem, as there are thousands of possible codes. In this working notes paper, we present our system and the results of the CLEF 2018 eHealth Evaluation Task 1 on Multilingual Information Extraction-ICD10 coding. This benchmark addresses information extraction in written text with focus on several languages, specifically Hungarian, Italian and French. The goal is to automatically assign ICD10 codes to diagnostic terms of death certificates. The problem can be cast in different ways, for example as a multilabel classification task or as sequence-to-sequence prediction. Our proposal follows this last approach, with promising results, well above the average results for the task. It only relies on the material provided by the task organizers, allowing the application of the same system to all datasets.
Present approaches of automated language scoring lack the ability to investigate the multiple-level and several contexts of sequential features which are helpful to examine the language proficiency for the responses (...
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Recommendation systems has emerged as an essential component in web-based systems, as their ability to analyze customers' behavior and generate recommendations seeking customers' satisfaction is successfully a...
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We present a live cross-lingual system capable of producing shallow semantic annotations of natural language sentences for 51 languages at this time. The domain of the input sentences is in principle unconstrained. Th...
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Fuzzy Logic systems have been successfully used in a wide range of real-world problems. They can include a priori expert knowledge and represent systems for which it is not possible to obtain a mathematical model. The...
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The rise of ubiquitous deepfakes, misinformation, disinformation and post-truth, often referred to as fake news, raises concerns over the role of Internet and social media in modern democratic societies. Due to its ra...
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In this work, we present a method for automatic colorization of grayscale videos. The core of the method is a Generative Adversarial Network that is trained and tested on sequences of frames in a sliding window manner...
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