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Music identification is widely regarded as a solved problem for music searching in quiet environments, but its performance tends to degrade in TV broadcast audio owing to the presence of dialogue or sound effects. In addition, constructing an accurate dataset for measuring the performance of background music monitoring in TV broadcast audio is challenging. We propose a framework for monitoring background music by automatic identification and introduce a background music cue sheet. The framework comprises three main components: music identification, music–speech separation, and music detection. In addition, we introduce the Cue‐K‐Drama dataset, which includes reference songs, audio tracks from 60 episodes of five Korean TV drama series, and corresponding cue sheets that provide the start and end timestamps of background music. Experimental results on the constructed and existing datasets demonstrate that the proposed framework, which incorporates music identification with music–speech separation and music detection, effectively enhances TV broadcast audio monitoring. [ABSTRACT FROM AUTHOR]

Experts in music therapy has suggested music as an aid to give the positive state of mind by keeping all sorts of depression and anxiety away. Music helps to bring back the original state of vibration by controlling our emotions [1]. A music is a combined effect of melody, the singer’s voice, and linguistics. The singer voice expresses the singer’s emotion like glad sorrow, anxiety, peace, tiredness and which in turns control the listener’s mental state. Indian music is analyzed and an approach for information retrieval to propose a therapeutic system through detection and identification of Indian music is initiated. Music Information Retrieval is a powerful tool to analyze different characteristics of a music. However, in this approach different traits of music are studied, and categorization has been done which leads to the therapeutic cause. [ABSTRACT FROM AUTHOR]

We have extended our Sports Video Browsing framework for Personal Video Recorders, such as Recordable-DVD Recorders, Blu-ray Disc Recorders and/or Hard Disc Recorders, to music segment detection. Our extension to Japanese broadcast music video programs consists of detecting audio segment boundaries such as conversations with guests followed by music/song etc. Our proposed system first identifies the music/song scenes using audio analysis, and then adjusts the start/end position by detecting video shot changes, so as to achieve accurate detection of the music segment thus enabling rapid browsing. Our preliminary results indicate that our audio-only summarization with scene change support works well for music video content. We can therefore integrate the enhancement into our product at a low computational cost. [ABSTRACT FROM AUTHOR]

We extend our Sports Video Browsing framework for Personal Video Recorders, such as DVD Recorders, Blu-ray Disc Recorders and/or Hard Disc Recorders, to other genres. We reduce the computational complexity by reducing the number of audio classes to a small but useful set that is useful for both sports video and music video, as well as by reducing the complexity of the Gaussian Mixture Models. Our extension to music video content consists of detecting music/song periods by compensating for false alarms. Our results indicate that our enhanced audio-only summarization maintains the sports video performance and works well with music video content. We can therefore integrate the enhancement into our product while in fact reducing the computational complexity. [ABSTRACT FROM AUTHOR]

We propose a new method for music detection from broadcasting contents using the convolutional neural networks with a Mel-scale kernel. In this detection task, music segments should be annotated from the broadcast data, where music, speech, and noise are mixed. The convolutional neural network is composed of a convolutional layer with kernel that is trained to extract robust features. The Mel-scale changes the kernel size, and the backpropagation algorithm trains the kernel shape. We used 52 h of mixed broadcast data (25 h of music) to train the convolutional network and 24 h of collected broadcast data (ratio of music of 50–76%) for testing. The test data consisted of various genres (drama, documentary, news, kids, reality, and so on) that are broadcast in British English, Spanish, and Korean languages. The proposed method consistently showed better performance in all the three languages than the baseline system, and the F-score ranged from 86.5% for British data to 95.9% for Korean drama data. Our music detection system takes about 28 s to process a 1-min signal using only one CPU with 4 cores. [ABSTRACT FROM AUTHOR]