Social media sharing platforms (e.g., YouTube, Flickr, Instagram, and SoundCloud) have revolutionized how users access multimedia content online. Most of these platforms provide a variety of ways for the user to interact with the different types of media: images, video, music. In addition to watching or listening to the media content, users can also engage with content in different ways, e.g., like, share, tag, or comment. Social media sharing platforms have become an important resource for scientific researchers, who aim to develop new indexing and retrieval algorithms that can improve users’ access to multimedia content. As a result, enhancing the experience provided by social media sharing platforms.
Historically, the multimedia research community has focused on developing multimedia analysis algorithms that combine visual and text modalities. Less highly visible is research devoted to algorithms that exploit an audio signal as the main modality. Recently, awareness for the importance of audio has experienced a resurgence. Particularly notable is Google’s release of the AudioSet, “A large-scale dataset of manually annotated audio events” [7]. In a similar spirit, we have developed the “Socially Significant Music Event“ dataset that supports research on music events [3]. The dataset contains Electronic Dance Music (EDM) tracks with a Creative Commons license that have been collected from SoundCloud. Using this dataset, one can build machine learning algorithms to detect specific events in a given music track.
What are socially significant music events? Within a music track, listeners are able to identify certain acoustic patterns as nameable music events. We call a music event “socially significant” if it is popular in social media circles, implying that it is readily identifiable and an important part of how listeners experience a certain music track or music genre. For example, listeners might talk about these events in their comments, suggesting that these events are important for the listeners (Figure 1).
Traditional music event detection has only tackled low-level events like music onsets [4] or music auto-tagging [8, 10]. In our dataset, we consider events that are at a higher abstraction level than the low-level musical onsets. In auto-tagging, descriptive tags are associated with 10-second music segments. These tags generally fall into three categories: musical instruments (guitar, drums, etc.), musical genres (pop, electronic, etc.) and mood based tags (serene, intense, etc.). The types of tags are different than what we are detecting as part of this dataset. The events in our dataset have a particular temporal structure unlike the categories that are the target of auto-tagging. Additionally, we analyze the entire music track and detect start points of music events rather than short segments like auto-tagging.
There are three music events in our Socially Significant Music Event dataset: Drop, Build, and Break. These events can be considered to form the basic set of events used by the EDM producers [1, 2]. They have a certain temporal structure internal to themselves, which can be of varying complexity. Their social significance is visible from the presence of large number of timed comments related to these events on SoundCloud (Figure 1,2). The three events are popular in the social media circles with listeners often mentioning them in comments. Here, we define these events [2]:
- Drop: A point in the EDM track, where the full bassline is re-introduced and generally follows a recognizable build section
- Build: A section in the EDM track, where the intensity continuously increases and generally climaxes towards a drop
- Break: A section in an EDM track with a significantly thinner texture, usually marked by the removal of the bass drum
![Figure 1. Screenshot from SoundCloud showing a list of timed comments left by listeners on a music track [11].](http://sigmm.hosting.acm.org/wp-content/uploads/2018/03/figure1_datasets.png)
Figure 1. Screenshot from SoundCloud showing a list of timed comments left by listeners on a music track [11].
SoundCloud
SoundCloud is an online music sharing platform that allows users to record, upload, promote and share their self-created music. SoundCloud started out as a platform for amateur musicians, but currently many leading music labels are also represented. One of the interesting features of SoundCloud is that it allows “timed comments” on the music tracks. “Timed comments” are comments, left by listeners, associated with a particular time point in the music track. Our “Socially Significant Music Events” dataset is inspired by the potential usefulness of these timed comments as ground truth for training music event detectors. Figure 2 contains an example of a timed comment: “That intense buildup tho” (timestamp 00:46). We could potentially use this as a training label to detect a build, for example. In a similar way, listeners also mention the other events in their timed comments. So, these timed comments can serve as training labels to build machine learning algorithms to detect events.
![Figure 2. Screenshot from SoundCloud indicating the useful information present in the timed comments. [11]](http://sigmm.hosting.acm.org/wp-content/uploads/2018/03/figure2_datasets1.png)
Figure 2. Screenshot from SoundCloud indicating the useful information present in the timed comments. [11]
SoundCloud also provides a well-documented API [6] with interfaces to many programming languages: Python, Ruby, JavaScript etc. Through this API, one can download the music tracks (if allowed by the uploader), timed comments and also other metadata related to the track. We used this API to collect our dataset. Via the search functionality we searched for tracks uploaded during the year 2014 with a Creative Commons license, which results in a list of tracks with unique identification numbers. We looked at the timed comments of these tracks for the keywords: drop, break and build. We kept the tracks whose timed comments contained a reference to these keywords and discarded the other tracks.
Dataset
The dataset contains 402 music tracks with an average duration of 4.9 minutes. Each track is accompanied by timed comments relating to Drop, Build, and Break. It is also accompanied by ground truth labels that mark the true locations of the three events within the tracks. The labels were created by a team of experts. Unlike many other publicly available music datasets that provide only metadata or short previews of music tracks [9], we provide the entire track for research purposes. The download instructions for the dataset can be found here: [3]. All the music tracks in the dataset are distributed under the Creative Commons license. Some statistics of the dataset are provided in Table 1.
Table 1. Statistics of the dataset: Number of events, Number of timed comments
| Event Name |
Total number of events |
Number of events per track |
Total number of timed comments |
Number of timed comments per track |
| Drop |
435 |
1.08 |
604 |
1.50 |
| Build |
596 |
1.48 |
609 |
1.51 |
| Break |
372 |
0.92 |
619 |
1.54 |
The main purpose of the dataset is to support training of detectors for the three events of interest (Drop, Build, and Break) in a given music track. These three events can be considered a case study to prove that it is possible to detect socially significant musical events, opening the way for future work on an extended inventory of events. Additionally, the dataset can be used to understand the properties of timed comments related to music events. Specifically, timed comments can be used to reduce the need for manually acquired ground truth, which is expensive and difficult to obtain.
Timed comments present an interesting research challenge: temporal noise. The timed comments and the actual events do not always coincide. The comments could be at the same position, before, or after the actual event. For example, in the below music track (Figure 3), there is a timed comment about a drop at 00:40, while the actual drop occurs only at 01:00. Because of this noisy nature, we cannot use the timed comments alone as ground truth. We need strategies to handle temporal noise in order to use timed comments for training [1].
![Figure 3. Screenshot from SoundCloud indicating the noisy nature of timed comments [11].](http://sigmm.hosting.acm.org/wp-content/uploads/2018/03/figure3_datasets1.png)
Figure 3. Screenshot from SoundCloud indicating the noisy nature of timed comments [11].
In addition to music event detection, our “Socially Significant Music Event” dataset opens up other possibilities for research. Timed comments have the potential to improve users’ access to music and to support them in discovering new music. Specifically, timed comments mention aspects of music that are difficult to derive from the signal, and may be useful to calculate song-to-song similarity needed to improve music recommendation. The fact that the comments are related to a certain time point is important because it allows us to derive continuous information over time from a music track. Timed comments are potentially very helpful for supporting listeners in finding specific points of interest within a track, or deciding whether they want to listen to a track, since they allow users to jump-in and listen to specific moments, without listening to the track end-to-end.
State of the art
The detection of music events requires training classifiers that are able to generalize over the variability in the audio signal patterns corresponding to events. In Figure 4, we see that the build-drop combination has a characteristic pattern in the spectral representation of the music signal. The build is a sweep-like structure and is followed by the drop, which we indicate by a red vertical line. More details about the state-of-the-art features useful for music event detection and the strategies to filter the noisy timed comments can be found in our publication [1].
Figure 4. The spectral representation of the musical segment containing a drop. You can observe the sweeping structure indicating the buildup. The red vertical line is the drop.
The evaluation metric used to measure the performance of a music event detector should be chosen according to the user scenario for that detector. For example, if the music event detector is used for non-linear access (i.e., creating jump-in points along the playbar) it is important that the detected time point of the event falls before, rather than after, the actual event. In this case, we recommend using the “event anticipation distance” (ea_dist) as a metric. The ea_dist is amount of time that the predicted event time point precedes an actual event time point and represents the time the user would have to wait to listen to the actual event. More details about ea_dist can be found in our paper [1].
In [1], we report the implementation of a baseline music event detector that uses only timed comments as training labels. This detector attains an ea_dist of 18 seconds for a drop. We point out that from the user point of view, this level of performance could already lead to quite useful jump-in points. Note that the typical length of a build-drop combination is between 15-20 seconds. If the user is positioned 18 seconds before the drop, the build would have already started and the user knows that a drop is coming. Using an optimized combination of timed comments and manually acquired ground truth labels we are able to achieve an ea_dist of 6 seconds.
Conclusion
Timed comments, on their own, can be used as training labels to train detectors for socially significant events. A detector trained on timed comments performs reasonably well in applications like non-linear access, where the listener wants to jump through different events in the music track without listening to it in its entirety. We hope that the dataset will encourage researchers to explore the usefulness of timed comments for all media. Additionally, we would like to point out that our work has demonstrated that the impact of temporal noise can be overcome and that the contribution of timed comments to video event detection is worth investigating further.
Contact
Should you have any inquiries or questions about the dataset, do not hesitate to contact us via email at: n.k.yadati@tudelft.nl
References
[1] K. Yadati, M. Larson, C. Liem and A. Hanjalic, “Detecting Socially Significant Music Events using Temporally Noisy Labels,” in IEEE Transactions on Multimedia. 2018. http://ieeexplore.ieee.org/document/8279544/
[2] M. Butler, Unlocking the Groove: Rhythm, Meter, and Musical Design in Electronic Dance Music, ser. Profiles in Popular Music. Indiana University Press, 2006
[3] http://osf.io/eydxk
[4] http://www.music-ir.org/mirex/wiki/2017:Audio_Onset_Detection
[5] https://developers.soundcloud.com/docs/api/guide
[6] https://developers.soundcloud.com/docs/api/guide
[7] https://research.google.com/audioset/
[8] H. Y. Lo, J. C. Wang, H. M. Wang and S. D. Lin, “Cost-Sensitive Multi-Label Learning for Audio Tag Annotation and Retrieval,” in IEEE Transactions on Multimedia, vol. 13, no. 3, pp. 518-529, June 2011. http://ieeexplore.ieee.org/document/5733421/
[9] http://majorminer.org/info/intro
[10] http://www.music-ir.org/mirex/wiki/2016:Audio_Tag_Classification
[11] https://soundcloud.com/spinninrecords/ummet-ozcan-lose-control-original-mix
