# A Global-local Attention Framework for Weakly Labelled Audio Tagging

###### Abstract

Weakly labelled audio tagging aims to predict the classes of sound events
within an audio clip, where the onset and offset times of the sound events are
not provided. Previous works have used the multiple instance learning (MIL)
framework, and exploited the information of the whole audio clip by MIL
pooling functions. However, the detailed information of sound events such as
their durations may not be considered under this framework. To address this
issue, we propose a novel two-stream framework for audio tagging by exploiting
the global and local information of sound events. The global stream aims to
analyze the whole audio clip in order to capture the local clips that need to
be attended using a class-wise selection module. These clips are then fed to
the local stream to exploit the detailed information for a better decision.
Experimental results on the AudioSet show that our proposed method can
significantly improve the performance of audio tagging under different
baseline network architectures.

Index Terms—  Audio tagging, weak labels, two-stream framework, class-wise
attentional clips

##  1 Introduction

Audio Tagging is a technique for predicting the presence or absence of sound
events within an audio clip [1]. The Detection and Classification of Acoustic
Scenes and Events (DCASE) challenges [2, 3] provide strongly labelled datasets
for audio tagging, where the onset and offset time of sound events are
annotated. However, such annotation is time-consuming and hard to obtain, and
these audio tagging datasets are relatively small. Recently, weakly labelled
audio tagging has attracted increasing interest in the audio signal processing
community [4, 5], where the datasets (e.g. AudioSet [6]) only annotate the
types of sound events present in each audio clip but do not provide any
timestamp information of their onset and offset.

As the duration of sound events can be very different and the overlaps of
sound events often occur in an audio clip, audio tagging with weakly labelled
data is a challenging problem. A popular approach for this problem is based on
multiple instance learning (MIL) [7, 8]. In MIL, the input sequence is treated
as a bag and split into the set of instances, where multiple instances in the
same bag share the same labels. There are two main MIL strategies, i.e.
instance-level approach [9] and embedding-level approach [10, 11]. The
embedding-level approach integrates the instance-level feature representations
into a bag-level contextual representation and then directly carries out bag-
level classification, which shows better performance than the instance-level
approach [12]. The methods for aggregating the information from the instances
play an important part in the MIL frameworks. The default choices are global
max pooling (GMP) [13] and global average pooling (GAP) [14], but they are
often less flexible for adapting to practical applications and may lose
detailed information relevant to acoustic events. For example, GMP cannot
capture the information of the long-duration event keyboard typing well, while
the short-duration event mouse click may be ignored by GAP. More recently,
attention mechanisms have been employed to detect the occurence of sound
events [15, 16, 17, 18] and have achieved promising results. However, these
methods attempt to make a decision on the whole audio clips and are limited in
capturing the detailed information of the acoustic events.

Fig. 1: Overall architecture of our two-stream framework for weakly labelled
audio tagging (GL-AT).

To address this issue, in this paper, we propose a novel global-local
attention (GL-AT) framework for weakly labelled audio tagging where the global
and local information are successively modelled in the audio clip. Our method
is inspired by the behaviors of human annotators for an audio dataset [6]. At
first, the annotators may glimpse over an audio clip roughly, and determine
some possible categories and their temporal regions. Then, these possible
regions guide them to make refined decisions on specific categories following
a region-by-region inspection. Thus, in a similar fashion, we can solve the
audio tagging with a two-stream framework, including a global stream and a
local stream. More specifically, the global stream takes an audio clip as the
input to a deep neural network and learns global representations supervised by
the weak labels. Several class-wise attentional sub-clips are then selected
according to the global representations, and fed to another neural network to
learn the local representations. The final class distributions are obtained by
aggregating the predicted global class distributions and local class
distributions. The optimization of the local stream is influenced by the
global stream because the sub-clips are selected according to the prediction
results of the global stream. In addition, the local stream improves the
optimization of the global stream where sub-clip selection and classification
are both performed.

The contributions of this paper can be summarized into three aspects. Firstly,
we present a two-stream global-local attention framework that can efficiently
recognize sound events within an audio clip with weakly labelled data.
Secondly, we propose an effective class-wise sub-clip selection module which
can dynamically generate several attentional sub-clips with low complexity and
high diversity. Thirdly, experimental results show that our proposed framework
can significantly improve the performance of AudioSet tagging, and can be used
in different baselines.

##  2 Proposed Method

In this section, we firstly present the two-stream framework for weakly
labelled audio tagging (i.e. GL-AT). Then, a class-wise clips selection module
is presented, which bridges the gap between the global and local streams.

###  2.1 GL-AT

Global Stream. The overall framework of our proposed framework is shown in
Fig. 1. Given an input audio clip $\boldsymbol{A}\in\mathbb{R}^{T_{0}\times
S}$, where $T_{0}$ and $S$ are the duration (e.g. $10$s) and the sampling
rate, respectively. Let’s denote its corresponding label as
$\boldsymbol{y}\in\mathbb{R}^{L}$, where $y^{i}=\\{0,1\\}$ denotes whether
label $i$ appears or not and $L$ denotes the number of labels.

The feature extractor $\mathcal{F}(\cdot)$ is firstly applied, which can be
either convolutional neural networks (CNNs) [19, 20, 21, 22, 23] or
convolutional recurrent neural networks (CRNNs) [9]. We assume that
$\boldsymbol{M}=\mathcal{F}\left(\boldsymbol{A};\theta_{\mathcal{F}}\right)$
is the global frame-wise feature after the feature extractor, where
$\theta_{\mathcal{F}}$ denotes the parameters of the feature extractor and
$\boldsymbol{M}\in\mathbb{R}^{T\times C}$. Here, $T$ and $C$ denote the number
of output frames and the dimension of the features for each frame,
respectively. Then a global pooling function $\mathcal{P}\left(\cdot\right)$
is applied to obtain the global clip-wise feature
$\boldsymbol{M^{\prime}}\in\mathbb{R}^{1\times C}$. Following [22], both
maximum and average operations are used for global pooling. In order to get
the prediction score $\hat{\boldsymbol{y}}\in\mathbb{R}^{L}$, a classifier
$\mathcal{C}(\cdot)$ containing two fully-connected layers is applied [22].

$\hat{\boldsymbol{y}}=\mathcal{C}\left(\boldsymbol{M^{\prime}};\theta_{\mathcal{C}}\right)$
(1)

where $\theta_{\mathcal{C}}$ denotes the parameters of the classifier. We then
use a sigmoid function $\sigma\left(\cdot\right)$ to turn
$\hat{\boldsymbol{y}}$ into a range $\left[0,1\right]$, and obtain the global
clip-wise prediction score
$\hat{\boldsymbol{y}}_{\boldsymbol{g}}\in\mathbb{R}^{L}$.

$\hat{\boldsymbol{y}}_{\boldsymbol{g}}=\frac{1}{1+exp\left(-\hat{\boldsymbol{y}}\right)}$
(2)

Local Stream. Let
$\left\\{\boldsymbol{A}_{1},\boldsymbol{A}_{2},\cdots,\boldsymbol{A}_{N}\right\\}$
be a set of $N$ local clips selected from the input audio clip
$\boldsymbol{A}$. These local clips have the same duration (but are shorter
than $\boldsymbol{A}$), and are fed to another feature extractor which has the
same structure as the global stream. Then the local prediction scores
$\left\\{\hat{\boldsymbol{y}}_{1},\hat{\boldsymbol{y}}_{2},\cdots,\hat{\boldsymbol{y}}_{N}\right\\}$
are obtained using (1) and (2). Finally, these local predicted scores are
aggregated by the global pooling function:

$\hat{\boldsymbol{y}}_{\boldsymbol{l}}=\mathcal{P}\left(\hat{\boldsymbol{y}}_{1},\hat{\boldsymbol{y}}_{2},\cdots,\hat{\boldsymbol{y}}_{N}\right)$
(3)

where $\hat{\boldsymbol{y}}_{\boldsymbol{l}}\in\mathbb{R}^{L}$ is the local
clip-wise prediction score. Note that this two-stream framework can be trained
end-to-end and transferred easily to different feature extractor networks.
During the training stage, these two streams are jointly trained. At the
inference stage, we fuse the predictions from the global stream
($\hat{\boldsymbol{y}}_{\boldsymbol{g}}$) and the local stream
($\hat{\boldsymbol{y}}_{\boldsymbol{l}}$) with the global pooling function to
generate the final prediction score of the audio.

Two-stream Learning. Given a training dataset
$\left\\{\boldsymbol{A}^{i},\boldsymbol{y}_{i}\right\\}_{i=1}^{D}$, where $D$
denotes the number of training examples. $\boldsymbol{A}^{i}$ is the $i$-th
audio clip and $\boldsymbol{y}_{i}$ represents its corresponding labels. The
overall loss function of our two-stream learning is formulated as the sum of
two streams,

$\boldsymbol{\mathcal{L}}=\boldsymbol{\mathcal{L}_{g}}+\boldsymbol{\mathcal{L}_{l}}$
(4)

where $\boldsymbol{\mathcal{L}_{g}}$ and $\boldsymbol{\mathcal{L}_{l}}$
represent the global and the local loss, respectively. Specifically, the
binary cross entropy loss is applied for both streams,

$\boldsymbol{\mathcal{L}_{g}}=\sum_{i=1}^{D}\sum_{j=1}^{L}y_{i}^{j}\log\left(\hat{y}_{gi}^{j}\right)+\left(1-y_{i}^{j}\right)\log\left(1-\hat{y}_{gi}^{j}\right)$
(5)
$\boldsymbol{\mathcal{L}_{l}}=\sum_{i=1}^{D}\sum_{j=1}^{L}y_{i}^{j}\log\left(\hat{y}_{li}^{j}\right)+\left(1-y_{i}^{j}\right)\log\left(1-\hat{y}_{li}^{j}\right)$
(6)

where $\hat{y}_{gi}^{j}$ and $\hat{y}_{li}^{j}$ are the prediction scores of
the $j$-th category of the $i$-th audio clip from the global stream and local
stream, respectively. The Adam [24] is employed as the optimizer.

###  2.2 From Global to Local

Potential audio clips are not available in weak labels, and in this paper, we
propose a simple but efficient method to dynamically generate candidate audio
clips following two basic principles. On the one hand, the diversity of
candidate clips should be as high as possible to cover all possible sound
events within an audio clip. On the other hand, the number of candidate clips
should be as small as possible to reduce computational complexity and storage
space.

In order to generate the candidate clips, we firstly calculate the class-wise
activation. A classifier is directly applied to the global frame-wise feature
$\boldsymbol{M}$, and the global frame-wise predicted score
$\boldsymbol{S}_{g}\in\mathbb{R}^{T\times L}$ can be obtained using (1) and
(2), where $T$ denotes the number of frames within an audio clip. The class-
wise activation of the $i$-th category is denoted as
$S_{g}^{i}\in\mathbb{R}^{T}$, which indicates the importance of the frames
leading to the classification of an audio clip to class $i$.

Such class-wise activation $\boldsymbol{S}_{g}$ is discriminative among
different categories, and we can employ the activation to localize the
potential candidate clips. However, there are often a large number of
categories (e.g. $527$ categories in AudioSet), and only a small number of
categories (less than $10$) appear in an audio clip. If the activation of all
the categories is used, the generated clips are too many to be computationally
efficient. To address this issue, we sort the predicted score
$\boldsymbol{S}_{g}$ in a descending order and select the top $N$ class-wise
attentional activation (denoted as $\left\\{S_{g}^{i}\right\\}_{i=1}^{N}$).
$N$ is a hyperparameter and the choice of $N$ is discussed in our experiments.

The value of $S_{g}^{i}(t)$ represents the probability that the sub-clip
belongs to the $i$-th category at timestamp $t$. In order to localize the
clips of interest with low computational complexity, we employ a temporal
window of size $\tau$ to select the candidate clips. For each $S_{g}^{i}(t)$,
the frame with the maximum activation (denoted as $m$) is set as the medium
frame, and the range of the candidate clip is set as
$\left[m-\tau/2,m+\tau/2\right]$. If the maximum boundary of the candidate
clip is over the duration of the audio clip, the range is re-weighted to
$\left[T_{0}-\tau,T_{0}\right]$, where $T_{0}$ denotes the duration of the
audio clip. In addition, if the minimum boundary is less than $0$, we re-
weight the range to $\left[0,\tau\right]$, where $\tau$ is a hyperparameter
and discussed in our experiments.

##  3 Experiments

In this section, we report the experimental results and comparisons that
demonstrate the effectiveness of the proposed method. In addition, ablation
studies are carried out to show the contribution of the crucial components.

###  3.1 Experimental Setups

Dataset. Audioset [6] is used in our experiments, which is a large-scale
dataset with over $2$ million $10$-second audio clips from YouTube videos,
with a total of $527$ categories. The same dataset divisions and pre-
processing approaches (e.g. re-sampling and data-balancing) are applied as
[22].

Metrics. Mean average precision (mAP), mean area under the curve (mAUC) and
d-prime are used as our evaluation metrics, which are the most commonly used
metrics for audio tagging. These metrics are calculated on individual
categories and then averaged across all categories.

Implementation Details. We compare the proposed method with the recent state-
of-the-arts including TALNet [9], CNN10 [22], ResNet38 [22] and AT-SCA [25].
Specifically, all these models are applied as the global stream of our
framework, and the local stream uses the same feature extractor and classifier
as in the global stream. Thus, we obtain the results of the models with our
two-stream learning framework, which are TALNet+⃝GL-AT, CNN10+⃝GL-AT,
ResNet38+⃝GL-AT and AT-SCA+⃝GL-AT. Batch size is set as $32$ and all networks
are trained with $800$k iterations in total. Unless otherwise stated, we set
the hyperparameters $N$ as $5$ and $\tau$ as $3$s in our experiments.

###  3.2 Comparsions with the State-of-the-Arts

Table 1 demonstrates the performance of our proposed method (GT-
AL111https://github.com/WangHelin1997/GL-AT) and other state-of-the-art
methods on the Audioset. The results indicate that the proposed GT-AL can
significantly improve the performance of all the compared methods, which
confirms the effectiveness of our two-stream learning framework. Among them,
TALNet [9] is a CRNN-based model, and CNN10 [22] and ResNet38 [22] are CNN-
based models. AT-SCA [25] applies the spatial and channel-wise attention to
detect sound events by learning what and where to attend in the signal.
However, all these methods attempted to make a decision once and aggregated
the global information by the MIL pooling functions. The aggregation often
cannot make good use of the detailed information because of the variability of
different sound events. By comparison, our method captures the discriminative
local information from the global information and then checks attentionally
the local information for a better decision, which provides a more efficient
way to exploit the weak labels.

Table 1: Accuracy comparisons of our method and state-of-the-arts on the AudioSet. Method | mAP | mAUC | d-prime
---|---|---|---
TAL Net (2019) [9] | 0.362 | 0.965 | 2.554
TAL Net∗ | 0.368 | 0.967 | 2.600
TALNet+⃝GL-AT (ours) | 0.401 | 0.970 | 2.659
CNN10 (2019) [22] | 0.380 | 0.971 | 2.678
CNN10∗ | 0.382 | 0.969 | 2.664
CNN10+⃝GL-AT (ours) | 0.408 | 0.974 | 2.742
ResNet38 (2019) [22] | 0.434 | 0.974 | 2.737
ResNet38∗ | 0.429 | 0.974 | 2.713
ResNet38+⃝GL-AT (ours) | 0.438 | 0.975 | 2.774
AT-SCA (2020) [25] | 0.390 | 0.970 | 2.652
AT-SCA∗ | 0.392 | 0.969 | 2.658
AT-SCA+⃝GL-AT (ours) | 0.413 | 0.971 | 2.677
∗The listed results of TAL Net, CNN10, ResNet38 and AT-SCA are reproduced, and
all the experimental setups are the same as the original papers [9, 22, 25].
Table 2:  Ablative study of two streams in CNN10+⃝GL-AT Method | Global | Local | mAP | mAUC
---|---|---|---|---
CNN10 | $\surd$ |  | 0.382 | 0.969
CNN10+⃝GL-AT | $\surd$ |  | 0.389 | 0.970
| $\surd$ | 0.400 | 0.972
$\surd$ | $\surd$ | 0.408 | 0.974

###  3.3 Ablation Study

In order to explore the effectiveness of the two streams, we jointly train the
global and local streams in GL-AT, and during the inference stage, the
influence of each stream is demonstrated in Table 2. Thanks to the joint
training strategy, GL-AT outperforms the baseline method (CNN10) with only the
global stream. This is because the class-wise attentional clips selection
module connects the two streams, and the optimization of the global stream is
influenced by the local stream, which leads to better robustness. In addition,
we can see that using the local stream alone performs better than only using
the global stream, for the reason that the local stream is able to focus on
the detailed information of the audio. Nonetheless, the global stream plays an
important role in guiding the learning of the local stream, and employing both
global and local streams achieves the best results in our experiments.

Furthermore, we exploit the influence of the number of the local clips ($N$)
and the duration of the local clips ($\tau$). As shown is Fig. 2, the mAP
performance shows an upward trend with the gradual increase in $N$. This means
that it is useful to improve the audio tagging performance with more local
clips. However, more clips will increase the computational cost because all
the selected clips are fed to the networks. We can see that the performance
tends to be stable when $N$ is set to $\left\\{5,6,7\right\\}$, and we set $N$
to $5$ for a balance of accuracy and complexity. In addition, we test
different $\tau$ values, and report the results in Fig. 2. As the values of
$\tau$ increase, the accuracy is boosted and then drops, which achieves high
accuracy when $\tau$ is $\left[3,4\right]$ (similar to [26]). We argue that
the length (i.e. about $3s$) is the duration of most sound events. If the
duration of the clips is too short (e.g. $1s$), the complete information of a
sound event cannot be captured. While on the other hand, if the duration is
too long (e.g. over $4s$), the detailed information will be compromised. The
extreme is that when the duration is $10s$ (the whole length of the audio
clip), the local stream works as the global stream, which does not capture the
detailed information intended.

Fig. 2:  Accuracy comparisons of CNN10+⃝GL-AT with different values of $N$ and
$\tau$ (metric: mAP)

##  4 Conclusions

We have presented a two-stream framework to take advantage of the global and
local information of audio, which resembles the multi-task learning principle.
Experimental results on the Audioset show that our method can boost the
performance of different state-of-the-art methods in audio tagging.

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