Numerical simulations offer a feasible alternative to the direct acoustic measurement of individual head-related transfer functions (HRTFs). For the acquisition of high quality 3D surface scans, as required for these simulations, several approaches exist. In this paper, we systematically analyze the variations between different approaches and evaluate the influence of the accuracy of 3D scans on the resulting simulated HRTFs. To assess this effect, HRTFs were numerically simulated based on 3D scans of the head and pinna of the FABIAN dummy head generated with 6 different methods. These HRTFs were analyzed in terms of interaural time difference, interaural level difference, energetic error in auditory filters and by their modeled localization performance. From the results, it is found that a geometric precision of about 1 mm is needed to maintain accurate localization cues, while a precision of about 4 mm is sufficient to maintain the overall spectral shape.

Automatically inferring the structural properties of raw multimedia documents is essential in today's digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework.

In this paper we investigate whether we can extract the commonalities shared by a group of cover songs or versions of the same musical piece. As a main contribution, we introduce the concept of cover group thumbnail, which is the most representative, essential subsequence for an entire group of versions. Opposed to previous approaches, we jointly consider all versions of a given song to compute a single cover group template, which then shows a high degree of robustness against version-specific aspects. To compute such a template, we introduce a modification of a recent audio thumbnailing technique. To evaluate the reliability of our conceptual contribution, we consider the task of template-based version identification, where we show comparable accuracies to existing systems.

The automatic extraction of structural information from music recordings constitutes a central research topic. In this paper, we deal with a subproblem of audio structure analysis called audio thumbnailing with the goal to determine the audio segment that best represents a given music recording. Typically, such a segment has many (approximate) repetitions covering large parts of the recording. As the main technical contribution, we introduce a novel fitness measure that assigns a fitness value to each segment that expresses how much and how well the segment “explains” the repetitive structure of the entire recording. The thumbnail is then defined to be the fitness-maximizing segment. To compute the fitness measure, we describe an optimization scheme that jointly performs two error-prone steps, path extraction and grouping, which are usually performed successively. As a result, our approach is even able to cope with strong musical and acoustic variations that may occur within and across related segments. As a further contribution, we introduce the concept of fitness scape plots that reveal global structural properties of an entire recording. Finally, to show the robustness and practicability of our thumbnailing approach, we present various experiments based on different audio collections that comprise popular music, classical music, and folk song field recordings.

Locating boundaries between coherent and/or repetitive segments of a time series is a challenging problem pervading many scientific domains. In this paper we propose an unsupervised method for boundary detection, combining three basic principles: novelty, homogeneity, and repetition. In particular, the method uses what we call structure features, a representation encapsulating both local and global properties of a time series. We demonstrate the usefulness of our approach in detecting music structure boundaries, a task that has received much attention in recent years and for which exist several benchmark datasets and publicly available annotations. We find our method to significantly outperform the best accuracies published so far. Importantly, our boundary approach is generic, thus being applicable to a wide range of time series beyond the music and audio domains.

In this paper, we investigate to which extent well-known audio fingerprinting techniques, which aim at identifying a specific audio recording, can be modified to also deal with more musical variations. To this end, we replace the standard peak fingerprints based on a spectrogram by peak fingerprints based on other more “musical” feature representations. Our systematic experiments show that such modified peak fingerprints allow for a robust identification of different versions and performances of the same piece of music if the query length is at least 15 seconds. This indicates that highly efficient audio fingerprinting techniques can also be applied to accelerate tasks such as audio matching or cover song identification.

The general goal of cross-version music retrieval is to identify all versions of a given piece of music by means of a short query audio fragment. To speed up the retrieval process, hashing techniques have been proposed, where the audio material is split up into small overlapping shingles (used as hashes) that consist of short feature subsequences. In this paper, we extend this work with the goal to minimize the number of hash lookups. To this end, one requires larger shingles that characterize the underlying piece of music to a high degree, while being robust to variations that occur across different versions. As our main contribution, we report on extensive experiments to highlight the delicate trade-off between the query length, feature parameters, shingle dimension, and index settings. These insights are of fundamental importance for building efficient cross-version retrieval systems that scale to millions of songs.

The extraction of tempo and beat information from music recordings constitutes a challenging task in particular for non-percussive music with soft note onsets and time-varying tempo. In this paper, we introduce a novel mid-level representation that captures musically meaningful local pulse information even for the case of complex music. Our main idea is to derive for each time position a sinusoidal kernel that best explains the local periodic nature of a previously extracted note onset representation. Then we employ an overlap-add technique accumulating all these kernels over time to obtain a single function that reveals the predominant local pulse (PLP). Our concept introduces a high degree of robustness to noise and distortions resulting from weak and blurry onsets. Furthermore, the resulting PLP curve reveals the local pulse information even in the presence of continuous tempo changes and indicates a kind of confidence in the periodicity estimation. As further contribution, we show how our PLP concept can be used as a flexible tool for enhancing tempo estimation and beat tracking. The practical relevance of our approach is demonstrated by extensive experiments based on music recordings of various genres.

The extraction of local tempo and beat information from audio recordings constitutes a challenging task, particularly for music that reveals significant tempo variations. Furthermore, the existence of various pulse levels such as measure, tactus, and tatum often makes the determination of absolute tempo problematic. In this paper, we present a robust mid-level representation that encodes local tempo information. Similar to the well-known concept of cyclic chroma features, where pitches differing by octaves are identified, we introduce the concept of cyclic tempograms, where tempi differing by a power of two are identified. Furthermore, we describe how to derive cyclic tempograms from music signals using two different methods for periodicity analysis and finally sketch some applications to tempo-based audio segmentation.

The general goal of music synchronization is to automatically align the multiple information sources such as audio recordings, MIDI files, or digitized sheet music related to a given musical work. In computing such alignments, one typically has to face a delicate tradeoff between robustness and accuracy. In this paper, we introduce novel audio features that combine the high temporal accuracy of onset features with the robustness of chroma features. We show how previous synchronization methods can be extended to make use of these new features. We report on experiments based on polyphonic Western music demonstrating the improvements of our proposed synchronization framework.