developer.biao.daily.20140901

尽管面临职业挑战,如未获得加薪或奖励,作者在过去九个月中取得了显著的进步,包括学习了多种编程语言(JavaScript、PHP 和 C#),开发了一些实用的应用程序,并开始撰写技术博客。

It is September now, which is another new month.

The 2014 is another tough year for me so far, although I exactly have done a lot of things this year.

The biggest issue now is I still do not get any salary increasing or prize for what I have done.

I did many things, but I gain nothing until now.

Yes, I should see the positive part as follow:

1. I learned new programming languages this year, that are JavaScript, PHP and C#. 

2. I have made some small application on different platform, for example,  the Accounting Robot, which can running on Chrome, Windows7, Mac OS. And some simple but really useful Web application like countWord, convertApp. 

3. I started this blog.

4. I translated a few English article.

5. I travelled to Romania and live there for 3 months.

6. I participated in the Hackathon in Timisoara, and won the first place with the whole team. And I find programming can really be awesome via this event.

7. I made a lot of good friends in Timisoara.

8. I get started learning database knowledge, and I can create and use simple database now.

9. I go back to do development work in the job.

... 

Besides all above highlights that happened in the nine months this year, I read some good books. I know coding horror blog this year. And another very important improvement for me in this year is that I get this sentence "Put the first thing first" and this word "focus".

I think I am lucky enough. I actually get enough good stuff this year.

...

Yep.... It is a great nine months for me. Maybe the only thing I should do is keeping patience.

The opportunity can be coming soon, I should be prepared.

I hope before the end of 2014 I can get a new job which can make me do something really cool and I can earn more money in this new job.



【语音分离】基于平均谐波结构建模的无监督单声道音乐声源分离(Matlab代码实现)内容概要:本文介绍了基于平均谐波结构建模的无监督单声道音乐声源分离方法,并提供了相应的Matlab代码实现。该方法通过对音乐信号中的谐波结构进行建模,利用音源间的频率特征差异,实现对混合音频中不同乐器或人声成分的有效分离。整个过程无需标注数据,属于无监督学习范畴,适用于单通道录音场景下的语音与音乐分离任务。文中强调了算法的可复现性,并附带完整的仿真资源链接,便于读者学习与验证。; 适合人群:具备一定信号处理基础和Matlab编程能力的高校学生、科研人员及从事音频处理、语音识别等相关领域的工程师;尤其适合希望深入理解声源分离原理并进行算法仿真实践的研究者。; 使用场景及目标:①用于音乐音频中人声与伴奏的分离,或不同乐器之间的分离;②支持无监督条件下的语音处理研究,推动盲源分离技术的发展;③作为学术论文复现、课程项目开发或科研原型验证的技术参考。; 阅读建议:建议读者结合提供的Matlab代码与网盘资料同步运行调试,重点关注谐波建模与频谱分解的实现细节,同时可扩展学习盲源分离中的其他方法如独立成分分析(ICA)或非负矩阵分解(NMF),以加深对音频信号分离机制的理解。
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