学术交流英语final Presentation

A Transcript for a Conference Paper Presentation Slide 1 (Title):

Time: 35 seconds (0:00-0:35)

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Good morning! My major is electric engineering. My research field is signal and information processing. The topic I concern is about speech enhancement. Today I will give a presentation with the title “A Two-stage Beamforming Approach for Noise Reduction and Dereverberation”. This work is done by Habets and Benesty from University of Quebec, Montreal, Canada.

Slide 2 (Introduction)

Time: 55 seconds (0:35-1:30)

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First, I will give a brief introduction of microphone arrays and make you have a preliminary

understanding of the research problem. Simply, when you place several microphones according to certain geometric shapes, you get a microphone array. Here is a liner array and a circle array is also general. Let’s take this picture as an example. There is a noise source at the location of the star. When the girl speaks, her sound will get captured by all microphones. In the same time, received signals are polluted by the undesired noise and we can’t have the clean speech.

Slide 3 (Introduction)

Time: 55 seconds (1:30-2:25)

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So, we need do something to solve this problem. In this slide, the background and significance of speech denosing and dereverberation are introduced. In many applications, such as speech recognition and teleconferencing, we need distant or hand-free audio acquisition. However, in many cases, we just receive a noise corrupted or reverberant version of desired speech signals. To achieve high-quality human-to-human or human-to-machine speech communication, we need to develop efficient noise reduction and dereverberation algorithms. Microphone arrays can be very useful in these situation.

Slide 4 (Body)

Time: 50 seconds (2:25-3:05)

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First, we can use beamforming for the microphone arrays during the processing of received signals. What is the beamforming? Here are its definition and working principle. Beamforming is a signal processing technique that applies microphone arrays for directional signal transmission or reception. By operating on received multichannel signals, beamforming allows us to recover signals from a particular direction and suppress noise signals from undesired directions. This technique is so called “beamforming”.

Slide 5 (Body)

Time: 45 seconds (3:05-3:55)

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In this paper, we use beamforming to achieve noise reduction and dereverberation. Noise reduction is important since noise is everywhere around us. Some common noise includes

machine noise, vehicle noise, music noise, babble noise, and so on. On the other hand, the reverberation is created when a sound is produced in an enclosed space causing a large number of echoes to build up and then slowly decay as the sound is absorbed by the walls and air.

Slide 6 (Body)

Time: 60 seconds (3:55-4:55)

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To achieve both noise reduction and dereverberation, the two-stage approach is proposed in this paper and before the noise reduction stage, a dereverberation stage is needed. Here is the principle diagram. These y represent the reserved signals by the microphone array and we have N microphone. These Q and H represent weighting coefficients of two different beamforming stages. The Z represents the final signal after noise reduction and dereverberation. In the next few slides, the details about how the algorithm work are given.

Slide 7 (Body)

Time: 45 seconds (4:55-5:45)

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The first stage is dereverberation stage. In this slide, the computational process of dereverberation stage is presented. All channel inputs are weighted. The weighted channel inputs are sent to the next stage for noise reduction. So the key is to find proper weights so that the reverberation components are minimized. This is implemented by complex mathematics computation. The final weights are independent on signals.

Slide 8 (Body)

Time: 45 seconds (5:45-6:25)

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On the basis, further analysis of the dereverberation stage is needed. The first stage comprises a signal-independent beamformer that generates a reference signal that contains a dereverberated version of the desired speech and residual interference. In general, the desired speech component at the output of the beamformer contains less reverberation compared to reverberant speech signal received at the microphones.

Slide 9 (Body)

Time: 40 seconds (6:25-6:55)

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The second stage is noise reduction stage. In this slide, the computational process of noise reduction stage is presented. The weighted inputs obtained in the first stage are again weighted and summed. The weights are computed so that the signal-to-noise ratio (usually called SNR) is maximized. Since SNR is independent on desired signals, the weights are independent on signals as well.

Slide 10 (Body)

Time: 45 seconds (6:55-7:25)

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Further analysis is also applied to the noise reduction stage is dereverberation stage. The second stage uses the filtered microphone signals and the noisy reference signal to estimate the

desired speech component at the output of the DS beamformer. A major advantage over classical approaches is that the proposed approach is able to dereverberate the received desired signal with very low speech distortion. This is the whole process of the proposed algorithm.

Slide 11 (Body)

Time: 20 seconds (7:25-7:45)

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Let’s see the performance. Here are two graphs which represent the received speech signal by one microphone and the processed speech signal by the proposed two-stage approach.

In the first graph, the fuzzy parts denote noise and reverberation. They has been weakened in the second graph. In this way, better performance is achieved.

Slide 12 (Conclusion)

Time: 20 seconds (7:45-8:05)

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In conclusion, our goal is to find a method which can achieve both dereverberation and noise reduction while causing low speech distortion as much as possible. After the introduction of the whole process of the proposed algorithm, let’s summarize what we have got.

Slide 13 (Conclusion)

Time: 10 seconds (8:05-8:25)

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First, a two-stage beanforming approach is designed. The first stage is a signal-independent beamformer that generates a reference signal which contains dereverberated version residual interference and the second stage is multichannel noise reduction to estimate the desired speech

component at the output of the first stage. Finally, better performance is observed.

Slide 14 (Conclusion)

Time: 25 seconds (8:25-8:50)

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Why is this significant? On the one hand, we need more recognizable and clearer speech instead of a noisy world. On the other hands, this work is significant in the future application of speech technologies. For example, you can make a telephone call without the mobile phone in hand. You can control your smart devices by voice even when you are few meters away.

Slide 15 (Conclusion)

Time: 15 seconds (8:50-9:00)

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In the future, we will implement proposed algorithm and design better speech enchantment algorithms. Thank you, are there any questions?