This paper considers a distance metric learning (DML) algorithm for a fingerprinting system, which identifies a query content by finding the fingerprint in the database (DB) that measures the shortest distance to the query fingerprint. For a given training set consisting of original and distorted fingerprints, a distance metric equivalent to the lpnorm of the difference of two linearly projected fingerprints is learned by minimizing the false-positive rate (probability of perceptually dissimilar content to be identified as being similar) for a given false-negative rate (probability of perceptually similar content to be identified as being dissimilar). The learned metric can perform better than the often used lpdistance and improve the robustness against a set of unexpected distortions. In the experiments, the distance metric learned by the proposed algorithm performed better than those metrics learned by well-known DML algorithms for classification.

This paper considers a method for learning a distance metric in a fingerprinting system which identifies a query content by measuring the distance between its fingerprint and a fingerprint stored in a database. A metric having a general form of the Mahalanobis distance is learned with the goal that the distance between fingerprints extracted from perceptually similar contents should be smaller than the distance between fingerprints extracted from perceptually dissimilar contents. The metric is learned by minimizing a cost function designed to achieve the goal. The cost function is convex, and the global minimum can be obtained using convex optimization. In our experiment, the distance metric learning is applied in an audio fingerprinting system, and it is experimentally shown that the learned distance metric improves the identification performance.

A novel binary audio fingerprint obtained by filtering and then quantizing the spectral centroids is proposed. A feature selection algorithm, coined pairwise boosting (PB), is used to determine the filters and quantizers by casting the fingerprinting problem of identifying a query audio clip into a binary classification problem. The PB algorithm selects the filters and quantizers which lead to accurate classification of matching and nonmatching audio pairs: a matching pair is an audio pair that should be classified as being identical, and a nonmatching pair is a pair that should be classified as being different. By iteratively reducing the classification error of both matching and nonmatching pairs, the PB algorithm improves both the robustness and discriminating ability. In our experiments, the proposed fingerprint outperformed previously reported binary fingerprints in terms of robustness and discriminating ability. In the experiment, we compared the performances of a number of distance measures.

This paper proposes a novel music genre classification system based on two novel features and a weighted voting. The proposed features, modulation spectral flatness measure (MSFM) and modulation spectral crest measure (MSCM), represent the time-varying behavior of a music and indicate the beat strength. The weighted voting method determines the music genre by summarizing the classification results of consecutive time segments. Experimental results show that the proposed features give more accurate classification results when combined with traditional features than the octave-based modulation spectral contrast (OMSC) does in spite of short feature vector and that the weighted voting is more effective than statistical method and majority voting.

In this paper, a fingerprint embedding method better-suited for the AND anti-collusion code (AND-ACC) is proposed. The proposed method embeds both a code and an orthogonal fingerprint using different basis vectors depending on the bit. Although the detection for the embedding method is complex, the performance of the fingerprinting system using proposed embedding method with the AND-ACC against average attack is improved compared with the AND-ACC fingerprinting scheme using code modulation embedding method. The system using the proposed embedding method is robust against the linear combination collusion attack (LCCA) whereas the system using the code modulation is not.

The performance of a fingerprinting system, which is often measured in terms of reliability and robustness, is directly related to the features that the system uses. In this letter, we present a new audio-fingerprinting method based on the normalized spectral subband moments. A threshold used to reliably determine a fingerprint match is obtained by modeling the features as a stationary process. The robustness of the normalized moments was evaluated experimentally and compared with that of the spectral flatness measure. Among the considered subband features, the first-order normalized moment showed the best performance for fingerprinting.

For image content authentication, a secure watermarking method using quantization-based embedding on the largest singular value (SV) is proposed. The block-wise quantization-based embedding can be vulnerable to vector quantization (VQ) attack and attacks associated with histogram analysis. To overcome these security problems, the proposed method places interdependency among image blocks and dithers the quantized value. By adjusting the threshold of the detector, a trade-off between the robustness to JPEG compression and the probability of misdetection can be made. The proposed method can detect a tampered area with high sensitivity. This is confirmed by experimental results and security analysis.

For multimedia fingerprinting, it is crucial to extract relevant features that allow direct access to the distinguishing characteristics of a multimedia object. Features used for fingerprinting directly relate to the performance of the entire fingerprinting system. The paper proposes a novel audio fingerprinting method based on normalized spectral subband centroids. The spectral subband centroid is selected due to its resilience against equalization, compression, and noise addition. Both reliability and robustness issues in the fingerprinting system are addressed. Experimental results show that the proposed method is not only reliable, but also robust against various audio processing steps, including MP3 compression, equalization, random start, time-scale modification, and linear speed change.

A novel transcoding algorithm for the adaptive multi rate (AMR) codec and the enhanced variable rate codec (EVRC) is proposed. In contrast to the conventional tandem transcoding algorithm, the proposed algorithm transcodes the parameters of one codec to the other without synthesizing the speech. The proposed algorithm decodes the parameters of source codec from the input bitstream, and based on frame classification and mode decision, it appropriately transforms the parameters of source codec to those of the target codec in the parametric domain. Finally, the transformed parameters are encoded into a bitstream that is decodable by the target codec. The parameters transcoded by the proposed algorithm are line-spectral pair (LSP), pitch delay, fixed codevector, codebook gains, and frame energy. Evaluation results show that while reducing both the computational complexity and delay by 50%, the proposed algorithm produces speech quality equivalent to that of produced by the tandem transcoding algorithm. The general idea is not restricted to the AMR and EVRC but is applicable to various other code-excited linear prediction (CELP) based codecs.

In this paper, we propose an efficient rate selection algorithm that can be used to transcode speech encoded by any code excited linear prediction (CELP)-type codec into a format compatible with selectable mode vocoder (SMV) via direct parameter transformation. The proposed algorithm performs rate selection using the CELP parameters. Simulation results show that while maintaining similar overall bit-rate compared to the rate selection algorithm of SMV, the proposed algorithm requires less computational load than that of SMV and does not degrade the quality of the transcoded speech.

In this paper, a novel transcoding algorithm for the Selectable Mode Vocoder (SMV) and the G.723.1 speech coder is proposed. In contrast to the conventional tandem transcoding algorithm, the proposed algorithm converts the parameters of one coder to the other without going through the decoding and encoding process. The proposed algorithm is composed of four parts: the parameter decoding, Line Spectral Pair (LSP) conversion, pitch period conversion and rate selection. The evaluation results show that the proposed algorithm achieves equivalent speech quality to that of tandem transcoding with reduced computational complexity and delay.