Tube internal erosion, which corresponds to its wall thinning process, is one of the major safety concerns for tubes. Many sensing technologies have been developed to detect a tube wall thinning process. Among them, fiber Bragg grating (FBG) sensors are the most popular ones due to their precise measurement properties. Most of the current works focus on how to design different types of FBG sensors according to certain physical laws and only test their sensors in controlled laboratory conditions. However, in practice, an industrial system usually suffers from harsh and dynamic environmental conditions, and FBG signals are affected by many unpredictable factors. Consequently, the FBG signals have more fluctuations and are polluted by noises. Hence, the signals no longer directly follow the assumed physical laws and their proposed thinning detection mechanisms no longer work. Targeting at this, this article develops a data-driven model for FBG signal feature extraction and tube wall thickness monitoring using data analytic techniques. In particular, we develop a spatiotemporal model to describe dynamic FBG signals and extract features related to thickness. By taking physical law as guideline, we trace the relationship between the extracted features and the tube wall thickness, based on which we construct an online statistical monitoring scheme for tube wall thinning process. We use both laboratory test and field trial experiment to demonstrate the efficacy and efficiency of the proposed scheme.Note to Practitioners—This article is motivated by the real industrial needs of inner erosion detection of tubes in harsh environment. Most of the current research works focus on designing various sensing apparatuses based on fiber Bragg grating (FBG) sensors for nondestructive erosion detection. These apparatuses prove to be able to collect signals reflecting tube wall thickness in static and controllable laboratory environment qualitatively. However, in reality, the industrial environment, which is impacted by many changing factors, is dynamic and uncontrollable. Consequently, the signals collected by these FBG apparatuses would have larger variations that mask the signals related to thickness. Furthermore, current methods have neither mentioned how to process their collected data to capture the unnoticeably slow but accumulative erosion information efficiently nor constructed online monitoring algorithms to detect the tube wall thinning process based on the collected signals quantitatively. Built upon their apparatuses but targeting at their unsolved challenges, we propose a novel data-driven approach for FBG signal analysis that can remove the environmental influence and extract features only related to tube wall thickness, and using the extracted features, we construct a statistical process control scheme to monitor tube wall thickness and detect erosion in real time efficiently.

Leveraging on the strength of energy-based processing for transient detection and pitch-based processing for softer onsets detection, we present a system that combines both energy and pitch cues for detecting onsets from different instrument categories. Given an audio input from an arbitrary instrument category, the system performs preliminary onset categorization based on the general note characteristics and then performs onsets integration based on the categorization. In addition, the proposed pitch processing technique explores musically relevant features extracted from the chromagram, which are robust for detecting pitch changes. The proposed system showed good detection performance on the MIREX audio onset detection dataset.

The development of effective music retrieval and recommendation applications requires meaningful features for characterizing music audio. The rhythm of music audio in particular, besides timbre and melody, is essential in describing the music piece. This paper illustrates how drum loop patterns characterize the salient rhythm structure of music and an approach for drum loop pattern extraction is described. The extracted drum loop patterns are formed based on the temporal regularity (accent on meter) and drum types (bass and snare) of the beats and are good representations of the rhythmic dimension of music. The extracted drum loop patterns are useful for music segmentation and further work would include music classification and similarity matching.

Although drum loops are widely present in many audio recordings of modern style music, there is little research that deals with automatic extraction of drum loops in polyphonic music audio. This paper presents an approach for drum loop pattern extraction, based on a technique of fusing the meter estimation information and onset clustering information. The extracted drum loop patterns are formed based on the temporal regularity (accent on meter) and drum types (bass and snare) of the beats. Despite not achieving very high-precision drum loop transcription, the extracted drum loop pattern characterizes the salient rhythm structure of the music, and can be very useful for categorization or similarity matching of music. Experimental results have shown the effectiveness of the proposed drum loop pattern extraction method.

Detecting the presence of musical sounds in broadcast audio is important for content-based indexing and retrieval of auditory and visual information in radio and TV programs. In this paper, we propose a novel approach for musical sounds detection in broadcast audio based on the analysis of the characteristic feature of musical tones, pitch tuning. A spectral analysis method is presented for detecting the evidence of pitch tuning in the audio signal. Unlike the existing methods for discriminating speech and music, the proposed technique is not limited by inadequate training data, and it can deal with the case of music mixed with speech. In addition, the technique can be efficiently implemented for real-time application. Experiments based on TRECVID data set have shown good performance of the proposed technique

Automatic music genre classification is one of the most challenging problems in music information retrieval and management of digital music database. In this paper, we propose a new framework using text category methods to classify music genres. This framework is different from current methods for music genre classification. In our framework, we consider music as text-like semantic music document, which is represented by a set of music symbol lexicons with a HMM (hidden Markov models) cluster. Music symbols can be seemed as high-level features or semantic features like beats or rhythms. We use latent semantic indexing (LSI) technique that is widely adopted in text categorization for music genre classification. From the experimental results, we could achieve an average recall over 70% for ten musical genres

The majority of pieces of music, including classical and popular music,are composed using music scales, such as keys. The key or the scale information of a piece provides important clues on its high level musical content, like harmonic and melodic context. Automatic key detection from music data can be useful for music classification, retrieval or further content analysis. Many researchers have addressed key finding from symbolically encoded music(MIDI); however, works for key detection in musical audio is still limited. Techniques for key detection from musical audio mainly consist of two steps:pitch extraction and key detection. The pitch feature typically characterizes the weights of presence of particular pitch classes in the music audio. In the existing approaches to pitch extraction, little consideration has been taken on pitch mistuning and interference of noisy percussion sounds in the audio signals, which inevitably affects the accuracy of key detection. In this paper, we present a novel technique of precise pitch profile feature extraction, which deals with pitch mistuning and noisy percussive sounds. The extracted pitch profile feature can characterize the pitch content in the signal more accurately than the previous techniques, thus lead to a higher key detection accuracy. Experiments based on classical and popular music data were conducted. The results showed that the proposed method has higher key detection accuracy than previous methods, especially for popular music with a lot of noisy drum sounds.

In this paper, an HMM-embedded unsupervised learning approach is proposed to detect the music events by grouping the similar segments of the music signal. This approach can cluster the segments based on their similarity of the spectral as well as the temporal structures. This is not easily done for clustering with the traditional similarity measures. Together with a Bayesian information criterion, the proposed approach can obtain a suitable event set to regularize the complexity of the model structure. The natural product of the approach is a set of music events modeled by the HMMs. Our experimental analyses show that the detected musical events have more perceptual meaning and are more consistent than the KL-distance based clustering. The learned events match better with our experience in spectrogram reading. Its capacity is further evaluated on a task of music identification. The identification error rate is reduced to 1.57%, and 56.3% relative error rate reduction is observed comparing with the system trained using the KL-distance clustering method.

Extracting the melody from polyphonic musical audio is a nontrivial research problem. This paper presents an approach for vocal melody extraction from dual channel Karaoke music audio. The extracted melody corresponds to the singing voice in the original performance channel, which can then be used for melody-based music retrieval. In the proposed technique, audio signals from both the accompaniment channel and the original performance channel are analyzed. The note partials are firstly extracted from the signal, which is represented in constant-Q transform frequency domain. Then the volume balance between the two channels is estimated based on signal approximation in the sub-bands. Finally, the pitch corresponding to the singing voice is identified based on the note partial differences between the two channels. The extracted melody is represented as a sequence of pitch values. This technique assumes that the two channels have similar accompaniment instrument performance except for the singing voices. Experimental result on 40 Karaoke music audios has shown the performance of the proposed technique. The pitch extraction rate is above 70% and melody retrieval accuracy in an 800-tune-database is 90%.

This paper presents a novel approach for content-based analysis of karaoke music, which utilizes multimodal contents including synchronized lyrics text from the video channel and original singing audio as well as accompaniment audio in the two audio channels. We proposed a novel video text extraction technique to accurately segment the bitmaps of lyrics text from the video frames and track the time of its color changes that are synchronized to the music. A technique that characterizes the original singing voice by analyzing the volume balance between the two audio channels is also proposed. A novel music structure analysis method using lyrics text and audio content is then proposed to precisely identify the verses and choruses of a song, and segment the lyrics into singing phrases. Experimental results based on 20 karaoke music titles of difference languages have shown that our proposed video text extraction technique can detect and segment the lyrics texts with accuracy higher than 90%, and the proposed multimodal approach for music structure analysis method has better performance than the previous methods that are based only on audio content analysis.

Browsing a digital video library can be very tedious especially with an ever expanding collection of multimedia material. We present a novel framework for extracting sub-window shots from MPEG encoded news video with the expectation that this will be another tool that can be used by retrieval systems. Sub-windows shots are also useful for tying in relevant material from multiple video sources. The system makes use of Macroblock parameters to extract visual features, which are then combined to identify possible sub-windows in individual frames. The identified sub-widows are then filtered by a non-linear Spatial-Temporal filter to produce sub-window shots. By working only on compressed domain information, this system avoids full frame decoding of MPEG sequences and hence achieves high speeds of up to 11 times real time.

The key or the scale information of a piece of music provides important clues on its high level musical content, like harmonic and melodic context, which can be useful for music classification, retrieval or further content analysis. Researchers have previously addressed the issue of finding the key for symbolically encoded music (MIDI); however, very little work has been done on key detection for acoustic music. In this paper, we present a method for estimating the root of diatonic scale and the key directly from acoustic signals (waveform) of popular and classical music. We propose a method to extract pitch profile features from the audio signal, which characterizes the tone distribution in the music. The diatonic scale root and key are estimated based on the extracted pitch profile by using a tone clustering algorithm and utilizing the tone structure of keys. Experiments on 72 music pieces have been conducted to evaluate the proposed techniques. The success rate of scale root detection for pop music pieces is above 90%.

Musical signals are highly structured. Untrained listeners can capture some particular musical events from audio signals. Uncovering this structure and detecting musical events will benefit musical content analysis. This is known to be an unsolved problem. In this paper, an unsupervised learning approach is proposed to automatically infer some structure of the music from segments generated by beat and onset analysis. A top-down clustering procedure is applied to group these segments into musical events with similar characteristics. A Bayesian information criterion is then used to regularize the complexity of the model structure. Experimental results show that this unsupervised learning approach can effectively group similar segments together and automatically determine the number of such musical events in a given music piece

This work presents a novel method for the automatic solmization of a melody, by which a melody (a sequence of MIDI notes) can be transcribed to sol-fa syllables (i.e., do, re, me, fa, sol, la, ti). Automatic solmization can assist in music skill training, music notation and content-based music retrieval. The proposed method is based on an approach for estimating the music scale of a melody. The key of the major scale ("do") is estimated using music scale models. Due to the diversity of melody types, models for both diatonic and pentatonic scales are employed to avoid the possible key ambiguity for folk songs. The decision of the key of a melody is based on the scale estimation results for aggregating music notes, so that the method can work for both short and long melodies. Experiments have shown that the technique can achieve 95% correct solmization of the melodies of pop songs.

Several time series matching techniques have been proposed for content-based music retrieval. These techniques have shown to be robust and effective for music retrieval by acoustic inputs, such as query-by-humming. However, due to the key transposition issue, all the current methods need to search a large space for the proper key in melody matching. This computation can be prohibitive for a practical music retrieval system with a large database.In this paper, we present a music scale modeling technique for melody matching. The root note of music scale (Major or Minor) of a melody is estimated by fitting the notes to a music scale model. The estimated root note can then be used as the key in melody matching. To the best of our knowledge, this is the first approach that utilizes music scale knowledge for retrieval. In our experiments, 96% of the songs in the database (3000 melodies) can fit into the music scale model. Promising results for query-by-humming retrieval have been obtained by using this novel approach.

In this paper, we present an algorithm for automatic music summarization. Spectral power, amplitude envelope and mel-cepstral coefficients are calculated as features to characterize the segmented frames of the music. Based on calculated features, a clustering technique is applied to group segmented frames into different clusters to structure the music content. Finally, the music summary is generated based on the clustering results and domain-specific music knowledge. The proposed algorithm is tested on different genres of music samples and the results of summarization are effective to actual expectation after listening evaluation by experienced users.

This paper proposes a method to embed and extract the watermark into and from digital compressed audio. The watermark is embedded in partially uncompressed domain and the embedding scheme is high related to audio content. The watermark embedding can be done very fast. The experimental result illustrates that the embedded watermark can survive the decoding and re-encoding process.

There have been several query-by-humming techniques developed for music retrieval. The techniques either are errorprone due to the inaccuracy of the hummed query or force the users to hum according to a metronome. This paper presents a new slope-based query-by-humming technique, in which the retrieval is robust to the inaccuracy in query and the use of metronome is eliminated. We use melody curve to represent the melodies of the original songs and the hummed query. And curve features: slope ranges and spans and note changes are extracted from the melody curves. Music retrieval is done by matching the curve features of query with those of the original musical songs. Results have shown the features and the algorithms are robust to humming inaccuracy.

A novel content-adaptive audio watermarking technique is proposed. To optimally balance in-audibility and robustness when embedding and extracting watermarks, the embedding scheme is high related to audio content by making use of the properties of human auditory system and multiple-bit hopping technique. The experimental results in robustness are provided to support all the novel features in our watermarking scheme.