Real Time Audio-Based Search in Media Files Using Machine Learning
نویسندگان
چکیده
This paper explores the various audio processing and matching methodologies available to extrapolate an algorithm which can be applied in real-time for effective audio extraction from audio-visual files and then searching for certain user defined audio patterns in said media file. With the exponential rise in multimedia content, the need to search and find information contained in these assets is a must. We propose to build tool which will enable the user to search across the spoken content of any audiovisual file chosen locally on his/her machine.
منابع مشابه
Reno Ringermute : An audio data mining toolkit
This thesis presents Ringermute, an application designed to support audio feature recognition and machine learning, from the training and testing to the deployment phase. By choosing from a combination of feature extraction routines provided by plug-ins, a researcher can quickly produce files for input to standard data mining tools. The best combination of feature-extraction and classifier plug...
متن کاملMultimedia Communication Quality Assessment Testbeds
We make an intensive use of multimedia frameworks in our research on modeling the perceived quality estimation in streaming services and real-time communications. In our preliminary work, we have used the VLC VOD software to generate reference audiovisual files with various degree of coding and network degradations. We have successfully built machine learning based models on the subjective qual...
متن کاملAudio Onset Detection Using Machine Learning Techniques: the Effect and Applicability of Key and Tempo Information
This paper explores the effect musical context, namely key and tempo, on audio onset detection using machine learning techniques, with a focus on the changes in performance caused by mismatched key and tempo between training and test pieces, and the potential benefits of incorporating such musical information. We extract frequency energy information from audio as the input instance attributes f...
متن کاملMusic Genre Classification using Machine Learning Techniques
Categorizing music files according to their genre is a challenging task in the area of music information retrieval (MIR). In this study, we compare the performance of two classes of models. The first is a deep learning approach wherein a CNN model is trained end-to-end, to predict the genre label of an audio signal, solely using its spectrogram. The second approach utilizes hand-crafted feature...
متن کاملJamming With Plunderphonics: Interactive Concatenative Synthesis Of Music
This paper proposes to use the techniques of Concatenative Sound Synthesis in the context of real-time Music Interaction. We describe a system that generates an audio track by concatenating audio segments extracted from pre-existing musical files. The track can be controlled in real-time by specifying high-level properties (or constraints) holding on metadata about the audio segments. A constra...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014