Socially significant music events

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 [810]. 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]:

  1. Drop: A point in the EDM track, where the full bassline is re-introduced and generally follows a recognizable build section
  2. Build: A section in the EDM track, where the intensity continuously increases and generally climaxes towards a drop
  3. 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].

Figure 1. Screenshot from SoundCloud showing a list of timed comments left by listeners on a music track [11].


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]

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.


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].

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.

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.


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.


Should you have any inquiries or questions about the dataset, do not hesitate to contact us via email at:


[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.

[2] M. Butler, Unlocking the Groove: Rhythm, Meter, and Musical Design in Electronic Dance Music, ser. Profiles in Popular Music. Indiana University Press, 2006 






[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.




Diversity and Credibility for Social Images and Image Retrieval

Social media has established itself as an inextricable component of today’s society. Images make up a large proportion of items shared on social media [1]. The popularity of social image sharing has contributed to the popularity of the Retrieving Diverse Social Images task at the MediaEval Benchmarking Initiative for Multimedia Evaluationa [2]. Since its introduction in 2013, the task has attracted a large participation and has published a set of datasets of outstanding value to the multimedia research community.

The task, and the datasets it has released, target a novel facet of multimedia retrieval, namely the search result diversification of social images. The task is defined as follows: Given a large number of images, retrieved by a social media image search engine, find those that are not only relevant to the query, but also provide a diverse view of the topic/topics behind the query (see an example in Figure 1). The features and methods needed to address the task successfully are complex and span different research areas (image processing, text processing, machine learning). For this reason, when creating the collections used in the Retrieving Diverse Social Images Tasks, we also created a set of baseline features. The features are released with the datasets. In this way, task participants who have expertise in one particular research area may focus on that area and still participate in the full evaluation.

Figure 1: Example of retrieval and diversification results for query “Pingxi Sky Lantern Festival” (results are truncated to the first 14 images for better visualization): (top images) Flickr initial retrieval results; (bottom images) diversification achieved with the approach from the TUW team (best approach at MediaEval 2015).

Figure 1: Example of retrieval and diversification results for query “Pingxi Sky Lantern Festival” (results are truncated to the first 14 images for better visualization): (top images) Flickr initial retrieval results; (bottom images) diversification achieved with the approach from the TUW team (best approach at MediaEval 2015).

The collections

Before describing the individual collections, it needs to be noted that all data consist of redistributable Creative Commons Flickr and Wikipedia content and are freely available for download (follow the instructions here [3]). Although the task ran also in 2017, we focus in the following on the datasets already released, namely: Div400, Div150Cred, Div150Multi and Div150Adhoc (corresponding to the 2013-2016 evaluation campaigns). Each of the four datasets available so far covers different aspects of the diversification challenge, either from the perspective of the task/use-case addressed, or from the data that can be used to address the task. Table 1 gives an overview of the four datasets that we describe in more detail over the next four subsections. Each of the datasets is divided into a development set and a test set. Although the division of development and test data is arbitrary, for comparability of results and full reproducibility, users of the collections are advised to maintain the separation when performing their experiments.

Table 1: Dataset statistics (devset – development data, testset – testing data, credibilityset – data for estimating user tagging credibility, single (s) – single topic queries, multi (m) – multi-topic queries, ++ – enhanced/updated content, POI – location point of interest, events – events and states associated with locations, general – general purpose ad-hoc topics).table1


In 2013, the task started with a narrowly defined use-case scenario, where a tourist, upon deciding to visit a particular location, reads the corresponding Wikipedia page and desires to see a diverse set of images from that location. Queries here might be “Big Ben in London” or “Palazzo delle Albere in Italy”. For each such query, we know the GPS coordinates, the name, and the Wikipedia page, including an example image of the destination. As a search pool, we consider the top 150 photos obtained from Flickr using the name as a search query. These photos come with some metadata (photo ID, title, description, tags, geotagging information, date when the photo was taken, owner’s name, number of times the photo has been displayed, URL in Flickr, license type, number of comments on the photo) [4].

In addition to providing the raw data, the collection also contains visual and text features of the data, such that researchers who are only interested in one of the two, can use the other without investing additional time in generating a baseline set of features.

As visual descriptors, for each of the images in the collection, we provide:

  • Global color naming histogram
  • Global histogram of oriented gradients
  • Global color moments on HSV
  • Global Locally Binary Patterns on gray scale
  • Global Color Structure Descriptor
  • Global statistics on gray level Run Length Matrix (Short Run Emphasis, Long Run Emphasis, Gray-Level Non-uniformity, Run Length Non-uniformity, Run Percentage, Low Gray-Level Run Emphasis, High Gray-Level Run Emphasis, Short Run Low Gray-Level Emphasis, Short Run High Gray-Level Emphasis, Long Run Low Gray-Level Emphasis, Long Run High Gray-Level Emphasis)
  • Local spatial pyramid representations (3×3) of each of the previous descriptors

As textual descriptors we provide the classic Term Frequency (TFt,d – the number of occurrences of term t in document d) and Document Frequency (DFt – the number of documents containing term t). Note that the datasets are not limited to a single notion of document. The most direct definition of a “document” is an image that can be either retrieved or not retrieved. However, it is easily conceivable that the relative frequency of a term in the set of images corresponding to one topic, or the set of images corresponding to one user might also be of interest in ranking the importance of a result to a query. Therefore, the collection also contains statistics that take a document to be a topic, as well as a user. All these are provided both as CSV files, as well as Lucene Index files. The former can be used as part of a custom weighting scheme, while the latter can be deployed directly in a Lucene/Solr search engine to obtain results based on the text without further effort.


The tourism use case also underlies Div150Cred, but a component addressing the concept of user tagging credibility is added. The idea here is that not all users tag their photos in a manner that is useful for retrieval and, for this reason, it makes sense to consider, in addition to the visual and text descriptors also used in Div400, another feature set – a user credibility feature. Each of the 153 topics (30 in the development set and 123 in the test set) comes therefore, in addition to the visual and text features of each image, with a value indicating the credibility of the user. This value is estimated automatically based on a set of features, so in addition to the retrieval development and test sets, DIV150Cred also contains a credibility set, used by us to generate the credibility of each user, and which can be used by any interested researcher to generate better credibility estimators.

The credibility set contains images for approximately 300 locations from 685 users (a total of 3.6 million images). For each user there is a manually assigned credibility score as well as an automatically estimated one, based on the following features:

  • Visual score – learned predictor of a user’s consistent and relevant tagging behavior
  • Face proportion
  • Tag specificity
  • Location similarity
  • Photo count
  • Unique tags
  • Upload frequency
  • Bulk proportion

For each of these, the intuition behind it and the actual calculation is detailed in the collection report [5].


Div150Multi adds another twist to the task of the search engine and its tourism use-case. Now, the topics are not simply points of interest, but rather a combination of a main concept and a qualifier, namely multi-topic queries about location specific events, location aspects or general activities (e.g., “Oktoberfest in Munich”, “Bucharest in winter”). In terms of features however, the collection builds on the existing ones used in Div400 and Div150Cred, but adds to the pool of resources the researchers have at their disposal. In terms of credibility, in addition to the 8 features listed above, we now also have:

  • Mean Photo Views
  • Mean Title Word Counts
  • Mean Tags per Photo
  • Mean Image Tag Clarity

Again, for details on the intuition and formulas behind these, the collection report [6] is the reference material.

A new set of descriptors has been now made available, based on convolutional neural networks.

  • CNN generic: a descriptor based on the reference convolutional (CNN) neural network model provided along with the Caffe framework [7]. This model is trained with the 1,000 ImageNet classes used during the ImageNet challenge. The descriptors are extracted from the last fully connected layer of the network (named fc7).
  • CNN adapted: These features were also computed using the Caffe framework, with the reference model architecture but using images of 1,000 landmarks instead of ImageNet classes. We collected approximately 1,200 Web images for each landmark and fed them directly to Caffe for training [8]. Similar to CNN generic, the descriptors were extracted from the last fully connected layer of the network (i.e., fc7).


For this dataset, the definition of relevance was expanded from previous years, with the introduction of even more challenging multi-topic queries unrelated to POIs. These queries address the diversification problem for a general ad-hoc image retrieval system, where general-purpose multi-topic queries are used for retrieving the images (e.g., “animals at Zoo”, “flying planes on blue sky”, “hotel corridor”). The Div150Adhoc collection includes most of the previously described credibility descriptors, but drops faceProportion and location-Similarity, as they were no longer relevant for the new retrieval scenario. Also, the visualScore descriptor was updated in order to keep up with the latest advancements on CNN descriptors. Consequently, when training individual visual models, the Overfeat visual descriptor is replaced by the representation produced by the last fully connected layer of the network [9]. Full details are available in the collection report [10].

Ground-truth and state-of-the-art

Each of the above collections comes with an associated ground-truth, created by human assessors. As the focus is on both relevance and diversity, the ground truth and the metrics used reflect it: Precision at cutoff (primarily P@20) is used for relevance, and Cluster Recall at cutoff (primarily CR@20) is used for diversity.

Figure 2 shows an overview of the results obtained by participants in the evaluation campaigns over the period 2013-2016, and serves as a baseline for future experiments on these collections. Results presented here are on the test set alone. The reader may find more information about the methods in the MediaEval proceedings, which are listed on the Retrieving Diverse Social Images yearly task pages on the MediaEval website (

Figure 2

Figure 2. Evolution of the diversification performance (boxplots — the interquartile range (IQR), i.e. where the 50% of the values are; the line within the box = median; the tails = 1.5*IQR; the points outside (+) = outliers) for the different datasets in terms of precision (P) and cluster recall (CR) at different cut-off values. Flickr baseline represents the initial Flickr retrieval result for the corresponding dataset.


The Retrieving Diverse Social Image task datasets, as their name indicates, address the problem of retrieving images taking into account both the need to diversify the results presented to the user, as well as the potential lack of credibility of the users in their tagging behavior. They are based on already state-of-the-art retrieval technology (i.e., the Flickr retrieval system), which makes it possible to focus on the challenge of image diversification. Moreover, the data sets are not limited to images, but rather also include rich social information. The credibility component, represented by the credibility subsets of the last three collections, is unique to this set of benchmark datasets.


The Retrieving Diverse Social Image task datasets were made possible by the effort of a large team of people over an extended period of time. The contributions of the authors were essential. Further, we would like to acknowledge the multiple team members who have contributed to annotating the images and making the MediaEval Task possible. Please see the yearly Retrieving Diverse Social Images task pages on the MediaEval website (


Should you have any inquires or questions about the datasets, don’t hesitate to contact us via email at: bionescu at imag dot pub dot ro.


[1] (last visited 2017-11-29).






[7] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding” in ACM International Conference on Multimedia, 2014, pp. 675–678.

[8] E. Spyromitros-Xioufis, S. Papadopoulos, A. L. Ginsca, A. Popescu, Y. Kompatsiaris, and I. Vlahavas, “Improving diversity in image search via supervised relevance scoring” in ACM International Conference on Multimedia Retrieval, 2015, pp. 323–330.

[9] K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, “Return of the devil in the details: Delving deep into convolutional nets” arXiv preprint arXiv:1405.3531, 2014.