Month: January 2019

From labeled to unlabeled data – on the data challenge in automatic drum transcription

by Chih-Wei Wu

Automatic Drum Transcription (ADT) is an on-going research topic that concerns the extraction of drum events from music signals. After roughly three decades of research on this topic, many methods and several datasets have been proposed to address this problem. However, similar to many other Music Information Retrieval (MIR) research topics, the availability of realistic and carefully curated datasets is one of the bottlenecks for advancing the performance of ADT systems.

In our previous blog post, we briefly discussed this challenge in the context of ADT. With a standard annotated dataset (i.e., ENST drums) and a small collection of unlabeled data, we demonstrated the possibility of harnessing unlabeled music data for improvements in the context of ADT.
In this paper, we explore this idea further in the following directions:

  1. Identify the major types of ADT systems and investigate generic methods for integrating unlabeled data into these systems accordingly
  2. Train the systems using a large scale unlabeled music dataset
  3. Evaluate all systems using multiple labeled datasets currently available

The intention is to validate the idea of using unlabeled data for ADT in a large scale. To achieve this goal, we present two approaches.

Method

To show that the benefit of using unlabeled data can be generalized to most ADT systems, the first thing is to identify the most popular ADT approaches. To this end, we reviewed existing ADT systems (for more information, please refer to this recent publication), and we found that the two most popular approaches can be categorized as Segment-and-classify ( classification-based) and Separate-and-detect (activation-based).

Based on these two types of approaches, two learning paradigms for incorporating unlabeled data are evaluated. These are

    1. Feature Learning and
    2. Student Teacher Learning.

As shown in the figure below, both paradigms may extract information from unlabeled data and transfer them to ADT systems through different mechanisms; the feature learning paradigm learns a feature extractor that computes distinctive features from audio signals, whereas the student teacher learning paradigm focuses on generating “pseudo ground truth” (i.e., soft targets) using teacher models and passing them onto student models. Different variants of both paradigms are evaluated.

Results 1: we need more labeled data

In the first part of the experiment results, the averaged performance across all evaluated systems for each labeled dataset (e.g, ENST drums, MIREX 2005, MDB drums, RBMA) is presented. As shown in the following figure, for each individual drum instrument such as Kick Drum (KD), Snare Drum (SD), and HiHat (HH), the averaged performances differ from dataset to dataset. This result not only shows the relative difficulties of these datasets, but also implies the danger of relying on solely one dataset (which is exactly the case in many prior ADT studies). This result highlights the need for more diverse labeled datasets!

Results 2: unlabeled data is useful

In the second part of the experiment results, different systems for each learning paradigm are compared under the controlled conditions (e.g., training methods, the number of unlabeled examples used, etc.). For feature learning paradigm, as shown in the following table, both evaluated systems outperformed baseline systems on SD for the averaged F-Measure. This improvement was confirmed through a statistical check. This result suggests Segment-and-classify ADT systems can successfully benefit from unlabeled data through feature learning.

Role System HH BD SD
Baseline MFCC 0.61 0.62 0.40
Baseline CONV-RANDOM 0.61 0.54 0.39
Evaluated CONV-AE 0.61 0.62 0.42
Evaluated CONV-DAE 0.61 0.61 0.42

For student-teacher learning paradigm, encouraging results can also be found (this time on HH). In the following table, it is shown that all student models performed better on HH compared to both teacher models. This result indicates the possibility of obtaining students that are better than teachers with the help of unlabeled data. Additionally, this result confirms the finding in our previous paper in a larger scale.

Role System HH BD SD
Teacher PFNMF (SMT) 0.47 0.61 0.45
Teacher PFNMF (200D) 0.47 0.67 0.40
Student FC-200 0.56 0.57 0.44
Student FC-ALL 0.53 0.59 0.42
Student FC-ALL (ALT 0.55 0.58 0.44

Conclusion

According to the results, both learning paradigms can potential improve the ADT performances with the addition of unlabeled data. However, each paradigm seems to benefit different instruments. In other words, it is not easy to conclude which paradigm is “the way to go” when it comes to harnessing unlabeled resources. To simply put it, unlabeled data certainly has potential in improving ADT systems, and further investigation is worthwhile.

If you are interested in learning more details about this work, please refer to our paper. The code is available on github.

On the evaluation of generative models in music

by Li-Chia Richard Yang

Generative modeling among creative systems has research interest in a wide variety of tasks. Just as deep learning has reshaped the whole field of artificial intelligence, it has reinvented generative modeling in recent years, e.g., in music or painting. Regardless, however, of the research interest in generative systems, the assessment and evaluation of such systems has proven challenging.

In recent research on music generation, various data-driven models have shown promising results. As a quick example, here are two generated samples from two distinct systems:

Magenta (Attention RNN)
Magenta (Lookback RNN)

 

Now, how can we analyze and compare the behavior of these models?
As the ultimate judge of creative output is the human (listener or viewer), subjective evaluation is generally preferable in generative modeling. However, the general drawbacks of subjective evaluation can be summarized as various issues related to the required amount of resources and to the experiment design. Furthermore, objective evaluation has the advantage of providing a systematic, repeatable measurement across a significant amount of generated samples.

The proposed evaluation strategy

The proposed method does not aim at assessing musical pieces in the context of human-level creativity nor does it attempt to model the aesthetic perception of music.

It rather applies the concept of multi-criteria evaluation in order to provide metrics that assess basic technical properties of the generated music and help researchers identify issues and specific characteristics of both model and dataset. In a first step, we define two collections of samples as our datasets (in case of objective evaluation, one dataset contains the generated samples, the other contains samples from the training dataset). Then, we extract a set of features based on musical domain-knowledge for two main targets of the proposed evaluation strategy:

Absolute MeasurEments

Absolute measurements give insights into properties and characteristics of a generated or collected set of data.
During the model design phase of a generative system, it can be of interest to investigate absolute metrics from the output of different system iterations or of datasets as opposed to a relative evaluation. A typical example is the comparison of the generated results from two generative systems: although the model properties cannot be determined precisely for a data-driven approach, the observation of the generated samples can justify or invalidate a system design.
For instance, an absolute measurement can be a statistic analysis of note length transition histogram of each sample of a given dataset. In the following figure, we can easily observe the difference of such this feature among datasets from two different genres.

Relative Measurements

In order to enable the comparison of different sets of data, the relative measure generalizes the result among features with various dimensions.
We first perform pairwise exhaustive cross-validation for each feature and smooth the histogram results into probability density functions (PDFs) for a more generalizable representation. If the cross-validation is computed within one set of data, we will refer to it as intra-set distances. If each sample of one set is compared with all samples of the other set, we call it the inter-set distances.
Finally, we measure the similarity between these distributions for the application of evaluating music generative systems, and compute two metrics between the target dataset’s intra-set PDF and the inter-set PDF: the Kullback-Leibler Divergence (KLD) and overlapped area (OA) of two PDFs.
Take the following visualized figure as an example: assume set1 is the training data, while set2 and set3 correspond to generated results from two systems. The analysis can provide a quick observation It can be easily observed that the system that generates generated set2 produces results more in line has a closer behavior in such feature with the training data (in the context of this feature).

Find out more

Check out our paper for detailed use-case demonstration and the released toolbox for further application.