# UCIL: An Unsupervised Class Incremental Learning Approach for
Sound Event Detection

###### Abstract

This work explores class-incremental learning (CIL) for sound event detection
(SED), advancing adaptability towards real-world scenarios. CIL’s success in
domains like computer vision inspired our SED-tailored method, addressing the
unique challenges of diverse and complex audio environments. Our approach
employs an independent unsupervised learning framework with a distillation
loss function to integrate new sound classes while preserving the SED model
consistency across incremental tasks. We further enhance this framework with a
sample selection strategy for unlabeled data and a balanced exemplar update
mechanism, ensuring varied and illustrative sound representations. Evaluating
various continual learning methods on the DCASE 2023 Task 4 dataset, we find
that our research offers insights into each method’s applicability for real-
world SED systems that can have newly added sound classes. The findings also
delineate future directions of CIL in dynamic audio settings.

Index Terms—  continual learning, sound event detection

##  1 Introduction

Sound event detection (SED) [1, 2] focuses on the automatic identification and
classification of various sound events within an audio stream. This technology
underpins a wide array of applications, from enhancing urban security systems
through the detection of anomalous noises to monitoring wildlife habitats by
recognizing species-specific calls [3, 4, 5]. The essence of SED lies in its
ability to parse continuous audio input, segment it into discrete events, and
accurately label each frame with one or more predefined sound classes.
Advances in deep learning have significantly propelled the capabilities of SED
systems, enabling them to perform effectively in controlled environments.
These systems generally operate under a closed set framework, where the model
is trained on a fixed dataset comprising predetermined sound classes. However,
there can be possibilities, where there emerges a need to deal with
recognition of new sound events from the perspective of the end-user for real-
world scenarios.

In response to the evolving nature of sound events in practical applications,
different strategies have been explored to incorporate new sound classes into
existing SED models. The most common approach is fine-tuning [6, 7], where the
model pretrained on the original set of classes is further trained using a
smaller dataset containing the new classes. While fine-tuning can facilitate
the integration of new knowledge [8, 9, 10], it often leads to catastrophic
forgetting, that affects performance of the model for the previously learned
classes as it accommodates new information. This limitation leads to a
challenge where the model struggles to learn new classes without losing
accuracy on the ones it already knows. Essentially, as the model tries to
absorb new information, its performance in previously learned classes degrades
significantly. The challenge is not just about introducing new classes; it’s
about enabling the model to expand its abilities over time while still holding
onto the knowledge it initially acquired.

The above discussed backdrop sets the stage for continual learning [11, 12]
that offers a novel paradigm designed to overcome limitations of a model
learned on a closed set of classes to continuously acquire new knowledge while
preserving existing knowledge. This approach counters catastrophic forgetting,
thereby ensuring that models adapt effectively over time. Class-incremental
learning (CIL) [13] is distinguished by its capacity to integrate new classes
into a model’s learning architecture sequentially. Unlike task-incremental
learning, which employs separate classifiers for each task, CIL utilizes an
expanding classifier, facilitating the model’s growth as new data becomes
available without the need for a full retraining.

CIL is pivotal in retaining previously learned knowledge, proving essential
for applications requiring ongoing model evolution. It has been effective in
areas such as computer vision (CV) [14, 15] and natural language
processing[16], significantly contributing their advancements. Its extension
into the audio [17] and speech domain [18], covering tasks like acoustic scene
classification (ASC) [19, 20] and keyword spotting[21], has proven the
method’s adaptability and utility in managing dynamic datasets. However, its
application in SED is relatively recent. While Koh et al. [22] have made
strides in adapting models for SED through transfer learning, it leaves the
scope for incremental learning to develop real-world SED systems.

This work introduces a novel CIL method for SED that aims to incorporate new
sound categories and maintain accurate detection of sound events. Drawing
inspiration from techniques used in ASC [19, 20] and CV [23, 24], we
implemented an unsupervised class incremental learning (UCIL) framework to
efficiently train the classifier on different tasks. We integrate a
distillation loss function that helps in retaining the classifier’s knowledge
of previously learned sound events while learning new ones, minimizing feature
differences between model iterations. Our strategy also includes a sample
selection process to leverage unlabeled data for improving the consistency of
SED model.

Further, we propose a technique to preserve previous examples by considering
the duration and frequency of events within each sound category ensuring a
balance. The proposed UCIL is evaluated on the DCASE 2023 Task 4A dataset with
comparison to some CIL methods. This work aims to contribute in the ongoing
development of CIL for audio processing considering SED as an application,
indicating future research directions for real-world scenarios amidst dynamic
audio settings.

##  2 Class Incremental Learning

###  2.1 Class Incremental Learning for SED

We introduce a CIL approach for SED, where the model learns to recognize a
variety of sounds expanded through a series of tasks
$\tau_{0},\tau_{1},\ldots,\tau_{N}$ each representing a different set of
sounds to be identified. This learning journey unfolds over $1+N$ time steps,
starting with an initial task $\tau_{0}$ and moving through $N$ subsequent
tasks, each introducing new sounds for detection. Every task $\tau_{i}$ has
its own dataset $X_{i}$, consisting of sound features $x_{j}^{i}$ and their
corresponding labels $y_{j}^{i}$ that are represented as multi-hot vectors.
The total number of sound classes $|C|$ evolves to include both existing
classes learned in previous tasks ($|C_{\text{ext}}|$) and the new classes
introduced in the current task ($|C_{\text{new}}|$).

Our model, designated as $P_{i}$ for each task $\tau_{i}$, comprises two main
components: a feature extractor $F_{i}^{\theta}$ and a classifier
$H_{i}^{\phi}$. The feature extractor analyzes the sound data to identify
distinguishing characteristics, while the classifier uses these features to
predict the types of sounds present.

###  2.2 Proposed UCIL Method

The proposed UCIL method comprises four integral components, which we discuss
in the following.

####  2.2.1 Independent Learning to Update Model by New Data

In our framework for SED, we start by training a learning model on a
fundamental task, which we refer to as $\tau_{0}$. This initial task uses a
dataset named $X_{0}$. To measure how well our model is learning, we employ
binary cross-entropy loss ($L_{BCE}$) that computes the difference between the
model predictions and the ground truth during both the initial learning stage
and later stages. It operates by applying a mathematical function, the sigmoid
function $\sigma$, to the model’s logits $o_{k}$ in class $k$ as:

$\displaystyle L_{BCE}=-\frac{1}{|C|}$
$\displaystyle\sum_{k=|C|}^{|C|}y_{k}^{i}\cdot\log(\sigma(o_{k}))$ (1)
$\displaystyle+(1-y_{k}^{i})\cdot\log(1-\sigma(o_{k})),$

After the initial task $\tau_{0}$, introducing the model to new sounds in
subsequent tasks $\tau_{i}$, we create an updated version of our model denoted
as $P_{i}$. We achieve this by adding the new classification heads of the
linear layer, building on the previous model $\hat{P}_{i-1}$ to accommodate
the new sound classes $|C_{\text{new}}|$ of the current task. In other words,
$P_{i}$ is an expanded version of $\hat{P}_{i-1}$, equipped with these new
classifiers. When we update the model, we focus only on enlarging its
classification ability to include the new sounds. The predictions made by the
model are now a mix of existing and new sound classes, which we refer to as
$o_{ext}$ and $o_{new}$, respectively. The updated model, with its new feature
analyzer $\hat{F}_{i}^{\theta}$ and classifier $\hat{H}_{i}^{\phi}$, is then
trained on the latest data set, $\hat{X}_{i}$, to handle the current SED task.
To prevent data imbalance issues of existing and new classes, we use
independent learning (IndL) [20] method, which allows the model to learn the
new sound predictions independently from the existing ones. Specifically, we
only compute the $L_{BCE}$ by applying the sigmoid function $\sigma$ to only
the new predictions. This way, the existing predictions are maintained
separately, helping the model to remember and recognize both existing and new
sounds effectively.

####  2.2.2 Temporal Knowledge Distillation from Existing to New

We use two special distillation losses to maintain the learned knowledge of
our model. The first one is output distillation loss $\mathcal{L}_{OD}$, which
reduces differences in the model’s initial predictions for known sound
classes, using mean squared error (MSE) loss. The second one is the feature
distillation loss $\mathcal{L}_{FD}$, which minimizes the gap between the
current and existing model’s feature extractor outputs by comparing their
alignment with cosine similarity. The current model $P_{i}$’s feature is the
output of the feature extractor $F_{i}^{\theta}$ given as
$v=F_{i}^{\theta}(x)$. In simpler terms, these techniques help our updated
model $P_{i}$ act like its existing version, $\hat{P}_{i-1}$, especially for
sounds it already learned.

$\displaystyle\mathcal{L}_{FD}=1-\cos(\text{norm}(v),\text{norm}(\bar{v}))$
(2) $\displaystyle\mathcal{L}_{OD}=MSE(\hat{P}_{i-1}(x)||o_{ext})$
$\displaystyle\mathcal{L}_{KD}=\mathcal{L}_{FD}+\lambda\mathcal{L}_{OD}$

where norm and $\cos$ denote the L2-normalization and cosine similarity,
respectively. In detail, we compute the scalar product with normalized vectors
to get the cosine similarity. The weight
$\lambda=\Omega\sqrt{\frac{|C|}{|C_{new}|}}$ is set adaptively, $\Omega$ is a
fixed constant, and $\lambda$ increases as the number of incremental phases
increases, to retain the knowledge of the increasing number of existing
classes in the model. For the existing sounds, we measure the output
predictions from $P_{i}$ and match them against $\hat{P}_{i-1}$.

For the new sound classes, we ensure that the model learns to extract
consistent features with the previous model. The model’s training involves a
mix of these methods, with a carefully tuned balance, to keep the model’s
knowledge up-to-date without forgetting existing information.

Figure 1: Block diagram of the proposed UCIL approach for task $\tau_{i}$. All
training data for task $\tau_{i}$ include the new data $X_{i}$, the rehearsal
data $x^{e}_{i}$, and the unlabeled data $x^{u}_{i}$. Bottom-right corner
shows how different data are used to predict different outputs.

####  2.2.3 Unsupervised Learning with Sample Selection

The essence of model distillation above in CIL is to ensure the capability for
the new version of a model is not too deviated from what it has learned
before. It resembles to the scenario of trying to make sure that a student’s
new knowledge does not overwrite what they already know. A common method to do
this involves some assumptions about the new and previous knowledge never
overlapping, but these assumptions often do not hold up in real-world
scenarios that leads the model to forget the previous knowledge [25].

To address this, we focus on making the predictions from the model more
consistent. We believe that by exposing the model to a wider variety of data,
it can better remember past learning. This is why we bring in extra, unlabeled
data $x^{u}_{i}$ into the dataset $X_{i}$ of the task$\tau_{i}$. However, not
all data is helpful—some can confuse the model. Hence, we have first come up
with a novel way to pick the most helpful samples from this additional data.
We look for samples where the model predictions differ greatly from what we
expect, assuming these samples will teach the model most effectively.
Specifically, we measure the discrepancy between two logits distributions with
L1 metric as:

$d=|\hat{P}_{i-1}(x^{u}_{i})-P_{i}(x^{u}_{i})|$ (3)

In practice, we take the first half of samples based on the discrepancy $d$
from our additional unlabeled data for each batch of training task $\tau_{i}$.
By selecting these samples by discrepancy carefully, we assist the model to
retain previously learned knowledge and become generalized from the past
knowledge to new situations.

We adapt $\mathcal{L}_{OD}$ to use as $\mathcal{L}_{UOD}$, focusing on
unlabeled data rather than the labeled data for $\mathcal{L}_{OD}$ as given in
Section 2.2.2 following our goal to reduce the gaps between the new and
existing models using this unlabeled data.

####  2.2.4 Balanced Memory Update Method

To further maintain the previous knowledge, especially in applications with
complex classes such as SED, we preserve some of the data from existing
classes in advance as the rehearsal data. We mix this data $x^{e}_{i}$ with
the incoming data of task $\tau_{i}$. The balanced sampling function is an
algorithm tailored for preparing rehearsal data in the context of SED using
strongly and weakly labeled data. It starts by computing the duration of sound
events, considering the difference between their onset and offset times. Then,
it aggregates these durations for each event, providing a measure of total
exposure for each sound in the strongly labeled dataset. For weakly labeled
data, the duration is 10 seconds for each event label.

The method makes sure if there is a fair mix of sounds for training the model.
It figures out how many examples of each sound are needed based on the seen
classes. It then picks a balanced set of sound examples from both types of
data to avoid any bias. The outcome is two rehearsal datasets, one from
strongly labeled and the other from weakly labeled data. This way, all seen
sounds get a fair chance to replay during training, leading to a more reliable
and fair SED model.

We adopt a multifaceted approach for independent unsupervised learning to
handle the challenge of integrating new sound classes without forgetting
previously learned information as illustrated in Figure 1. The total loss
function of the updated model is given as:

$\mathcal{L}_{tot}=\mathcal{L}_{BCE}+\mathcal{L}_{KD}+\mathcal{L}_{UOD}$ (4)

In summary, the model is initially trained on a foundational task and then
updated through subsequent tasks with new classifier units to enhance its
classification capabilities, employing independent learning to segregate the
learning of new and existing sound classes. To preserve accumulated knowledge
of the model, we use output and feature distillation losses, minimizing
discrepancies between the model versions and ensuring consistency across all
sound classes. Additionally, we incorporate unsupervised learning with
strategic sample selection from unlabeled datasets to expose the model to a
broader range of data, thus improving its generalization capabilities. Lastly,
our balanced memory update method prepares rehearsal data from both strongly
and weakly labeled datasets, ensuring a fair representation of all sound
classes to mitigate data bias.

##  3 Experiment Setting

###  3.1 Dataset, Task Setting and Performance Metric

In this study, we use the DCASE 2023 Task 4A dataset [26], a comprehensive
collection of 10-second audio clips categorized into 10 sound events. It
comprises of 1,578 real recordings with weak annotations, 14,412 real
recordings without labels, and 10,000 synthetic recordings that are strongly
labeled [27]. All clips were resampled to 16 kHz mono and segmented using a
2048-sample window and 256-sample hop length for spectrogram extraction and
log-mel spectrogram generation.

We devised two incremental task settings to assess our proposed method.
Firstly, the two-task setting randomly divides the 10 target sound classes in
the dataset into two groups, each containing 5 classes. This approach is
designed to test how well our method can adapt from one group of sounds to
another, a process known as source-target domain adaptation in SED. Secondly,
the four-task setting is inspired by an existing multi-task learning strategy
[28], that organizes the 10 target classes into 4 groups based on their
acoustic characteristics: [vacuum cleaner, frying, blender], [electric
shaver/toothbrush, running water], [speech, dog, cat], and [dishes, alarm bell
ringing]. We shuffle the groups as the four tasks to investigate the effect of
the number of tasks on the model’s ability to distinguish between different
sounds, providing insights into the complexities of learning and recognizing
diverse acoustic events.

We used the threshold independent polyphonic sound event detection scores
(PSDS) [29, 30] metric following the DCASE 2023 Task 4A protocol across two
scenarios for system evaluation. PSDS1 evaluates the system’s ability for
precise timing in detecting sound events, crucial for real-time applications,
whereas PSDS2 focuses on the system’s capability to distinguish between
similar sound classes, addressing class confusion in SED. The final
performance for the two settings is evaluated after the last task is learned.

###  3.2 Implementation Details and Reference Baselines

In our experiments, we used a batch size of 48 and the Adam [31] optimizer
with an initial 0.001 learning rate, gradually increasing over the first 50 of
200 total epochs following DCASE 2023 Task 4A setup. We adopted a mean teacher
model strategy [32] to enhance training stability, applying an exponential
moving average with a factor of 0.999. We consider a convolutional recurrent
neural network (CRNN) [33] model that serves as the DCASE baseline with about
1.2M parameters for all the studies in this work. We implemented early
stopping at 50 epochs to prevent overfitting, and set the weight $\Omega$ for
the output distillation loss ($L_{OD}$) at 2, ensuring a balance between
retaining existing knowledge and learning new information. We built following
5 reference baselines:

Table 1: Performance comparison of our proposed UCIL method with other CIL
methods using the two-task settings. Rehearsal size indicates the maximum clip
numbers of [Strongly Labeled, Weakly Labeled] as the rehearsal data in each
task.

Method | Rehearsal Size | PSDS1 | PSDS2
---|---|---|---
Finetune (lower bound) | \ | 0 | 0.086
Joint (upper bound) | \ | 0.354 | 0.565
EWC | [4000, 400] | 0.255 | 0.443
LwF | [4000, 400] | 0.247 | 0.459
NR | [4000, 400] | 0.216 | 0.416
UCIL | [4000, 400] | 0.260 | 0.466
UCIL | [2000, 200] | 0.237 | 0.434
UCIL | [1000, 100] | 0.217 | 0.417

  * •

Fine-tune Training: It acts as our lower-bound baseline, where the CRNN model
is simply adjusted for new tasks without any strategies to prevent forgetting
existing classes, demonstrating minimum expected performance.

  * •

NR (Naive Rehearsal) [34]: It randomly selects samples from previous tasks for
retraining, aiming to maintain memory through repetition.

  * •

EWC (Elastic Weight Consolidation) [35]: This introduces a penalty on changing
important parameters for past tasks, determined by the Fisher information
matrix, to balance new learning and memory retention.

  * •

LwF (Learning Without Forgetting) [36]: It updates the model for new tasks
while ensuring existing class performance is maintained by training on new
classes and reinforcing consistent predictions for existing ones.

  * •

Joint Training: Our upper-bound baseline trains the CRNN model with the entire
dataset, showing the maximum potential performance without incremental task
setting. In incremental task setting, the performance can never surpass the
joint training as it already knows the whole target classes the first time.

##  4 Results and Analysis

###  4.1 Results for Two-task and Four-Task Settings

We first conduct the studies under the two-task setting on the DCASE 2023 Task
4A dataset, whose results are reported in Table 1. We consider random sampling
to add the rehearsal data in EWC and LwF methods for a fair comparison. We
focus on demonstrating how CIL minimizes the performance gap due to
catastrophic forgetting. It observed that the lower-bound finetune approach
faces challenges in adapting to new tasks without retention strategies as
expected. The PSDS score is observed to be zero which indicates the model
forgot most of the previous knowledge and similar observation was also
reported in [20]. Analyzing the impact of different CIL strategies between
scenarios, it is evident that each method’s ability to handle the nuances of
prompt detection (PSDS1) versus class confusion (PSDS2) varies. Our UCIL
method, particularly with the highest rehearsal size, shows notable
performance, closely following the upper bound in both scenarios. This
highlights the competence of UCIL in rapidly reacting to new sounds while
effectively managing class distinctions. The performance of UCIL method across
different rehearsal sizes also provides insights into its scalability. While a
decrease in rehearsal size leads to a slight reduction in PSDS, UCIL maintains
competitive performance. This is particularly significant on comparing UCIL to
other CIL approaches such as EWC, LwF, and NR, where UCIL consistently
outperforms or matches their scores, specifically for PSDS2.

Table 2: Performance comparison of our proposed UCIL method with other CIL
methods using the four-task settings. Rehearsal size is varied similar to two-
task setting.

Method | Rehearsal Size | PSDS1 | PSDS2
---|---|---|---
Finetune (lower bound) | \ | 0 | 0.054
Joint (upper bound) | \ | 0.354 | 0.565
EWC | [2000, 200] | 0.137 | 0.251
LwF | [2000, 200] | 0.107 | 0.172
NR | [2000, 200] | 0.100 | 0.152
UCIL | [2000, 200] | 0.198 | 0.366
UCIL | [1000, 200] | 0.184 | 0.332
UCIL | [1000, 100] | 0.165 | 0.319

We then conduct the studies for four-task setting that are reported in Table
2. It is observed that UCIL demonstrates a remarkable performance, especially
notable in the PSDS2 scores, where its ability to minimize class confusion
gets highlighted. With a rehearsal size of [2000, 200], UCIL’s PSDS2 score
doubles that of NR significantly outperforms other CIL methods like EWC and
LwF. Moreover, the comparison between UCIL’s performance with different
rehearsal sizes provides valuable insights into its efficiency. Despite a
reduction in the amount of strongly labeled data, UCIL maintains commendable
performance, showcasing its effective use of data under a higher task number
based CIL studies.

Table 3: Ablation studies for UCIL method with different settings. All
experiments are with [1000, 200] rehearsal data size.

Method | FD | UL | MU | PSDS1 | PSDS2
---|---|---|---|---|---
UCIL | ✓ | ✓ | ✓ | 0.184 | 0.332
UCIL | ✓ | ✓ |  | 0.081 | 0.112
UCIL | ✓ |  | ✓ | 0.073 | 0.143
UCIL |  | ✓ | ✓ | 0.110 | 0.193

###  4.2 Ablation Study

Table 3 shows an ablation study of our UCIL method, examining the significance
of each components under the four-task setting. Each experiment is conducted
with a fixed rehearsal size of [1000, 200] for strongly and weakly labeled
data, providing a controlled environment to isolate the impact of each module:
feature distillation (FD), unsupervised learning (UL), and memory update (MU).
We find that the best performance is achieved when all components are active
(FD, UL, MU). Removing MU leads to a notable drop in scores, highlighting MU’s
critical role in effectively integrating new information while retaining
previous knowledge. Similarly, excluding UL also decreases performance,
underscoring UL’s importance in leveraging unlabeled data for broader
learning. Excluding FD while keeping UL and MU active shows a lesser but still
notable decline in performance, highlighting FD’s contribution to maintaining
model consistency and effectiveness across varied sound events.

##  5 Conclusion

In this work, we proposed a novel method for CIL referred to as UCIL to
advance SED research. Incorporating strategies such as independent learning,
temporal knowledge distillation and unsupervised sample selection, our method
stands out for its ability to maintain model consistency and leverage
unlabeled data effectively. The balanced update mechanism further ensures
equitable representation of sound classes. The studies on the DCASE 2023 Task
4 dataset, not only showcases the practical applicability of UCIL, but also
sets a stage for future research in dynamic audio environments.

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